research papers on computer science

Help | Advanced Search

Computer Science (since January 1993)

For a specific paper , enter the identifier into the top right search box.

• new (most recent mailing, with abstracts)
• recent (last 5 mailings)
• current month's cs listings
• specific year/month: 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 all months 01 (Jan) 02 (Feb) 03 (Mar) 04 (Apr) 05 (May) 06 (Jun) 07 (Jul) 08 (Aug) 09 (Sep) 10 (Oct) 11 (Nov) 12 (Dec)
• Catch-up: Changes since: 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 01 (Jan) 02 (Feb) 03 (Mar) 04 (Apr) 05 (May) 06 (Jun) 07 (Jul) 08 (Aug) 09 (Sep) 10 (Oct) 11 (Nov) 12 (Dec) 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 , view results without with abstracts
• Search within the cs archive
• Article statistics by year: 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993

Categories within Computer Science

• cs.AI - Artificial Intelligence ( new , recent , current month ) Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
• cs.CL - Computation and Language ( new , recent , current month ) Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
• cs.CC - Computational Complexity ( new , recent , current month ) Covers models of computation, complexity classes, structural complexity, complexity tradeoffs, upper and lower bounds. Roughly includes material in ACM Subject Classes F.1 (computation by abstract devices), F.2.3 (tradeoffs among complexity measures), and F.4.3 (formal languages), although some material in formal languages may be more appropriate for Logic in Computer Science. Some material in F.2.1 and F.2.2, may also be appropriate here, but is more likely to have Data Structures and Algorithms as the primary subject area.
• cs.CE - Computational Engineering, Finance, and Science ( new , recent , current month ) Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
• cs.CG - Computational Geometry ( new , recent , current month ) Roughly includes material in ACM Subject Classes I.3.5 and F.2.2.
• cs.GT - Computer Science and Game Theory ( new , recent , current month ) Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
• cs.CV - Computer Vision and Pattern Recognition ( new , recent , current month ) Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
• cs.CY - Computers and Society ( new , recent , current month ) Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
• cs.CR - Cryptography and Security ( new , recent , current month ) Covers all areas of cryptography and security including authentication, public key cryptosytems, proof-carrying code, etc. Roughly includes material in ACM Subject Classes D.4.6 and E.3.
• cs.DS - Data Structures and Algorithms ( new , recent , current month ) Covers data structures and analysis of algorithms. Roughly includes material in ACM Subject Classes E.1, E.2, F.2.1, and F.2.2.
• cs.DB - Databases ( new , recent , current month ) Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
• cs.DL - Digital Libraries ( new , recent , current month ) Covers all aspects of the digital library design and document and text creation. Note that there will be some overlap with Information Retrieval (which is a separate subject area). Roughly includes material in ACM Subject Classes H.3.5, H.3.6, H.3.7, I.7.
• cs.DM - Discrete Mathematics ( new , recent , current month ) Covers combinatorics, graph theory, applications of probability. Roughly includes material in ACM Subject Classes G.2 and G.3.
• cs.DC - Distributed, Parallel, and Cluster Computing ( new , recent , current month ) Covers fault-tolerance, distributed algorithms, stabilility, parallel computation, and cluster computing. Roughly includes material in ACM Subject Classes C.1.2, C.1.4, C.2.4, D.1.3, D.4.5, D.4.7, E.1.
• cs.ET - Emerging Technologies ( new , recent , current month ) Covers approaches to information processing (computing, communication, sensing) and bio-chemical analysis based on alternatives to silicon CMOS-based technologies, such as nanoscale electronic, photonic, spin-based, superconducting, mechanical, bio-chemical and quantum technologies (this list is not exclusive). Topics of interest include (1) building blocks for emerging technologies, their scalability and adoption in larger systems, including integration with traditional technologies, (2) modeling, design and optimization of novel devices and systems, (3) models of computation, algorithm design and programming for emerging technologies.
• cs.FL - Formal Languages and Automata Theory ( new , recent , current month ) Covers automata theory, formal language theory, grammars, and combinatorics on words. This roughly corresponds to ACM Subject Classes F.1.1, and F.4.3. Papers dealing with computational complexity should go to cs.CC; papers dealing with logic should go to cs.LO.
• cs.GL - General Literature ( new , recent , current month ) Covers introductory material, survey material, predictions of future trends, biographies, and miscellaneous computer-science related material. Roughly includes all of ACM Subject Class A, except it does not include conference proceedings (which will be listed in the appropriate subject area).
• cs.GR - Graphics ( new , recent , current month ) Covers all aspects of computer graphics. Roughly includes material in all of ACM Subject Class I.3, except that I.3.5 is is likely to have Computational Geometry as the primary subject area.
• cs.AR - Hardware Architecture ( new , recent , current month ) Covers systems organization and hardware architecture. Roughly includes material in ACM Subject Classes C.0, C.1, and C.5.
• cs.HC - Human-Computer Interaction ( new , recent , current month ) Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
• cs.IR - Information Retrieval ( new , recent , current month ) Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
• cs.IT - Information Theory ( new , recent , current month ) Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
• cs.LO - Logic in Computer Science ( new , recent , current month ) Covers all aspects of logic in computer science, including finite model theory, logics of programs, modal logic, and program verification. Programming language semantics should have Programming Languages as the primary subject area. Roughly includes material in ACM Subject Classes D.2.4, F.3.1, F.4.0, F.4.1, and F.4.2; some material in F.4.3 (formal languages) may also be appropriate here, although Computational Complexity is typically the more appropriate subject area.
• cs.LG - Machine Learning ( new , recent , current month ) Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
• cs.MS - Mathematical Software ( new , recent , current month ) Roughly includes material in ACM Subject Class G.4.
• cs.MA - Multiagent Systems ( new , recent , current month ) Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
• cs.MM - Multimedia ( new , recent , current month ) Roughly includes material in ACM Subject Class H.5.1.
• cs.NI - Networking and Internet Architecture ( new , recent , current month ) Covers all aspects of computer communication networks, including network architecture and design, network protocols, and internetwork standards (like TCP/IP). Also includes topics, such as web caching, that are directly relevant to Internet architecture and performance. Roughly includes all of ACM Subject Class C.2 except C.2.4, which is more likely to have Distributed, Parallel, and Cluster Computing as the primary subject area.
• cs.NE - Neural and Evolutionary Computing ( new , recent , current month ) Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
• cs.NA - Numerical Analysis ( new , recent , current month ) cs.NA is an alias for math.NA. Roughly includes material in ACM Subject Class G.1.
• cs.OS - Operating Systems ( new , recent , current month ) Roughly includes material in ACM Subject Classes D.4.1, D.4.2., D.4.3, D.4.4, D.4.5, D.4.7, and D.4.9.
• cs.OH - Other Computer Science ( new , recent , current month ) This is the classification to use for documents that do not fit anywhere else.
• cs.PF - Performance ( new , recent , current month ) Covers performance measurement and evaluation, queueing, and simulation. Roughly includes material in ACM Subject Classes D.4.8 and K.6.2.
• cs.PL - Programming Languages ( new , recent , current month ) Covers programming language semantics, language features, programming approaches (such as object-oriented programming, functional programming, logic programming). Also includes material on compilers oriented towards programming languages; other material on compilers may be more appropriate in Architecture (AR). Roughly includes material in ACM Subject Classes D.1 and D.3.
• cs.RO - Robotics ( new , recent , current month ) Roughly includes material in ACM Subject Class I.2.9.
• cs.SI - Social and Information Networks ( new , recent , current month ) Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
• cs.SE - Software Engineering ( new , recent , current month ) Covers design tools, software metrics, testing and debugging, programming environments, etc. Roughly includes material in all of ACM Subject Classes D.2, except that D.2.4 (program verification) should probably have Logics in Computer Science as the primary subject area.
• cs.SD - Sound ( new , recent , current month ) Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
• cs.SC - Symbolic Computation ( new , recent , current month ) Roughly includes material in ACM Subject Class I.1.
• cs.SY - Systems and Control ( new , recent , current month ) cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.

• solidarity - (ua) - (ru)
• news - (ua) - (ru)
• donate - donate - donate

for scientists:

• ERA4Ukraine
• Assistance in Germany
• Ukrainian Global University
• #ScienceForUkraine

default search action

• combined dblp search
• author search
• venue search
• publication search

Welcome to dblp

Search dblp, full-text search, please enter a search query.

• How to use the dblp search?
• Which technology does dblp use for searching the website?
• case-insensitive prefix search: default e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space e.g., codd model
• boolean or: connect words by pipe symbol (|) e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

Author search results

Venue search results, refine list.

refine by author

• temporarily not available

refine by venue

refine by type

refine by year

Publication search results

found 7,201,723 matches

skipping 7,201,723 more matches

failed to load more results, please try again later

• by publisher

• books & theses
• reference works
• edited collections

read as PDF

(read full post)

(updated 2023-06-28) A few days ago, we discussed the new dataset publications in dblp. As a preparation for more and more detailed datasets we slightly modify the DTD that defines the structure of our XML data export. A quick reminder: you can download the dblp dataset as a single XML […]

Datasets and other research artifacts are a major topic in the scientific community in the recent years. Many ongoing projects focus on improving the standardization, publication and citation of these artifacts. Currently, the dblp team is involved in three of them: NFDI4DataScience, NFDIxCS, and Unknown Data. As part of these […]

On November 4, 2022, the Joint Science Conference (GWK) selected Schloss Dagstuhl – Leibniz Center for Informatics and the consortium NFDIxCS for federal and state funding within the German National Research Data Infrastructure (NFDI). The consortium will be funded in the double-digit millions of Euros and over a duration of five […]

In the six months since the release of the dblp RDF dump and its persistent snapshot releases, the RDF dump has been downloaded a total of about a thousand times. We are pleased to see that the community is interested in using our semantic data in their research and beyond. […]

more blog posts

The dblp computer science bibliography provides open bibliographic information on major computer science journals and proceedings. Originally created at the University of Trier in 1993, dblp is now operated and further developed by Schloss Dagstuhl . For more information check out our F.A.Q.

dblp statistics

• # of publications : 7,201,723
• # of authors : 3,491,266
• # of conferences : 6,602
• # of journals : 1,871

more statistics

Related resources

• ACM Digital Library
• IEEE Xplore | CSDL
• Semantic Scholar
• INSPIRE-HEP

more external links

Social media links

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default . You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.

Unpaywalled article links

load links from unpaywall.org

Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy .

Archived links via Wayback Machine

load content from archive.org

Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy .

Reference lists

load references from crossref.org and opencitations.net

Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org , opencitations.net , and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy , as well as the AI2 Privacy Policy covering Semantic Scholar.

Citation data

load citations from opencitations.net

Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.

OpenAlex data

load data from openalex.org

Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex .

retrieved on 2024-04-22 16:34 CEST from data curated by the dblp team

see also: Terms of Use | Privacy Policy | Imprint

dblp was originally created in 1993 at:

since 2018, dblp has been operated and maintained by:

the dblp computer science bibliography is funded and supported by:

Subscribe to the PwC Newsletter

Join the community, trending research, lcb-net: long-context biasing for audio-visual speech recognition.

The growing prevalence of online conferences and courses presents a new challenge in improving automatic speech recognition (ASR) with enriched textual information from video slides.

Sound Multimedia Audio and Speech Processing

Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs

In this letter, we address the problem of enabling LLMs to comprehend Area Graph, a text-based map representation, in order to enhance their applicability in the field of mobile robotics.

Nezha: Deployable and High-Performance Consensus Using Synchronized Clocks

steamgjk/nezha • 3 Jun 2022

Nezha bridges the gap between protocols such as Multi-Paxos and Raft, which can be readily deployed and protocols such as NOPaxos and Speculative Paxos, that provide better performance, but require access to technologies such as programmable switches and in-network prioritization, which cloud tenants do not have.

Distributed, Parallel, and Cluster Computing Databases Networking and Internet Architecture C.2.1; C.2.4; C.4

Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU

We introduce Stream-K, a work-centric parallelization of matrix multiplication (GEMM) and related computations in dense linear algebra.

Data Structures and Algorithms Distributed, Parallel, and Cluster Computing

AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario

This allows the researchers to explore different aspects in meeting processing, ranging from individual tasks such as speech front-end processing, speech recognition and speaker diarization, to multi-modality modeling and joint optimization of relevant tasks.

Sound Audio and Speech Processing

A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement

xiph/rnnoise • 24 Sep 2017

Despite noise suppression being a mature area in signal processing, it remains highly dependent on fine tuning of estimator algorithms and parameters.

Safe Low-Altitude Navigation in Steep Terrain with Fixed-Wing Aerial Vehicles

ethz-asl/terrain-navigation • 9 Jan 2024

Fixed-wing aerial vehicles provide an efficient way to navigate long distances or cover large areas for environmental monitoring applications.

Body Design and Gait Generation of Chair-Type Asymmetrical Tripedal Low-rigidity Robot

shin0805/chair-typeasymmetricaltripedalrobot • 9 Apr 2024

In this study, a chair-type asymmetric tripedal low-rigidity robot was designed based on the three-legged chair character in the movie "Suzume" and its gait was generated.

CARLA-Autoware-Bridge: Facilitating Autonomous Driving Research with a Unified Framework for Simulation and Module Development

tumftm/carla-autoware-bridge • 17 Feb 2024

In addition to component tests, the safety assessment of individual modules also requires a holistic view at system level, which can be carried out efficiently with the help of simulation.

Tightly Joining Positioning and Control for Trustworthy Unmanned Aerial Vehicles Based on Factor Graph Optimization in Urban Transportation

roboticspolyu/ipn_mpc • 4 Oct 2023

Given the fact that the system positioning and control are highly correlated with each other, for example, the system dynamics of the control can largely help with the positioning, this paper proposed a joint positioning and control method (JPCM) based on factor graph optimization (FGO), which combines sensors' measurements and control intention.

Search code, repositories, users, issues, pull requests...

Provide feedback.

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly.

To see all available qualifiers, see our documentation .

• Notifications

Papers from the computer science community to read and discuss.

papers-we-love/papers-we-love

Folders and files, repository files navigation.

Papers We Love ( PWL ) is a community built around reading, discussing and learning more about academic computer science papers. This repository serves as a directory of some of the best papers the community can find, bringing together documents scattered across the web. You can also visit the Papers We Love site for more info.

Due to licenses we cannot always host the papers themselves (when we do, you will see a 📜 emoji next to its title in the directory README) but we can provide links to their locations.

If you enjoy the papers, perhaps stop by a local chapter meetup and join in on the vibrant discussions around them. You can also discuss PWL events, the content in this repository, and/or anything related to PWL on our Discord server.

Let us know if you are interested in starting one in your city!

All of our meetups follow our Code of Conduct .

Past Presentations

Check out our YouTube channel for videos and video playlists.

We're looking for pull requests related to papers we should add, better organization of the papers we do have, and/or links to other paper-repos we should point to.

Other Good Places to Find Papers

• 2 Minute Papers
• Bell System Technical Journal, 1922-1983
• Best Paper Awards in Computer Science
• Google Scholar (choose a subcategory)
• Microsoft Research
• Functional Programming Books Review
• MIT's Artificial Intelligence Lab Publications
• MIT's Distributed System's Reading Group
• arXiv Paper Repository
• Services Engineering Reading List
• Readings in Distributed Systems
• Gradual Typing Bibliography
• Security Data Science Papers
• Research Papers from Robert Harper, Carnegie Mellon University
• Lobste.rs tagged as PDF
• The Morning Paper

Please check out our wiki-page for links to blogs, books, exchanges that are worth a good read.

How To Read a Paper

Reading a paper is not the same as reading a blogpost or a novel. Here are a few handy resources to help you get started.

• How to read an academic article
• Advice on reading academic papers
• How to read and understand a scientific paper
• Should I Read Papers?
• The Refreshingly Rewarding Realm of Research Papers
• How to read a paper

Applications/Ideas built around Papers We Love

• Love a Paper - @loveapaper

Download papers

Open your favourite terminal and run:

This will scrape markdown files for links to PDFs and download papers to their respective directories.

See README.md for more options.

Contributing Guidelines

Please take a look at our CONTRIBUTING.md file.

The name "Papers We Love" and the logos for the organization are copyrighted, and under the ownership of Papers We Love Ltd, all rights reserved. When starting a chapter, please review our guidelines and ask us about using the logo.

Code of conduct

Contributors 250.

• Shell 100.0%

Open research in computer science

Spanning networks and communications to security and cryptology to big data, complexity, and analytics, SpringerOpen and BMC publish one of the leading open access portfolios in computer science. Learn about our journals and the research we publish here on this page. 

Highly-cited recent articles

Spotlight on.

EPJ Data Science

See how EPJ Data Science  brings attention to data science 

Reasons to publish in Human-centric Computing and Information Sciences

Download this handy infographic to see all the reasons why Human-centric Computing and Information Sciences is a great place to publish. 

We've asked a few of our authors about their experience of publishing with us.

What authors say about publishing in our journals:

Fast, transparent, and fair.  - EPJ Data Science Easy submission process through online portal. - Journal of Cloud Computing Patient support and constant reminder at every phase. - Journal of Cloud Computing Quick and relevant. - Journal of Big Data ​​​​​​​

How to Submit Your Manuscript

Your browser needs to have JavaScript enabled to view this video

Computer science blog posts

Read the latest from the SpringerOpen blog

The SpringerOpen blog highlights recent noteworthy research of general interest published in our open access journals. 

Failed to load RSS feed.

computer science Recently Published Documents

Total documents.

• Latest Documents
• Most Cited Documents
• Contributed Authors
• Related Sources
• Related Keywords

Hiring CS Graduates: What We Learned from Employers

Computer science ( CS ) majors are in high demand and account for a large part of national computer and information technology job market applicants. Employment in this sector is projected to grow 12% between 2018 and 2028, which is faster than the average of all other occupations. Published data are available on traditional non-computer science-specific hiring processes. However, the hiring process for CS majors may be different. It is critical to have up-to-date information on questions such as “what positions are in high demand for CS majors?,” “what is a typical hiring process?,” and “what do employers say they look for when hiring CS graduates?” This article discusses the analysis of a survey of 218 recruiters hiring CS graduates in the United States. We used Atlas.ti to analyze qualitative survey data and report the results on what positions are in the highest demand, the hiring process, and the resume review process. Our study revealed that a software developer was the most common job the recruiters were looking to fill. We found that the hiring process steps for CS graduates are generally aligned with traditional hiring steps, with an additional emphasis on technical and coding tests. Recruiters reported that their hiring choices were based on reviewing resume’s experience, GPA, and projects sections. The results provide insights into the hiring process, decision making, resume analysis, and some discrepancies between current undergraduate CS program outcomes and employers’ expectations.

A Systematic Literature Review of Empiricism and Norms of Reporting in Computing Education Research Literature

Context. Computing Education Research (CER) is critical to help the computing education community and policy makers support the increasing population of students who need to learn computing skills for future careers. For a community to systematically advance knowledge about a topic, the members must be able to understand published work thoroughly enough to perform replications, conduct meta-analyses, and build theories. There is a need to understand whether published research allows the CER community to systematically advance knowledge and build theories. Objectives. The goal of this study is to characterize the reporting of empiricism in Computing Education Research literature by identifying whether publications include content necessary for researchers to perform replications, meta-analyses, and theory building. We answer three research questions related to this goal: (RQ1) What percentage of papers in CER venues have some form of empirical evaluation? (RQ2) Of the papers that have empirical evaluation, what are the characteristics of the empirical evaluation? (RQ3) Of the papers that have empirical evaluation, do they follow norms (both for inclusion and for labeling of information needed for replication, meta-analysis, and, eventually, theory-building) for reporting empirical work? Methods. We conducted a systematic literature review of the 2014 and 2015 proceedings or issues of five CER venues: Technical Symposium on Computer Science Education (SIGCSE TS), International Symposium on Computing Education Research (ICER), Conference on Innovation and Technology in Computer Science Education (ITiCSE), ACM Transactions on Computing Education (TOCE), and Computer Science Education (CSE). We developed and applied the CER Empiricism Assessment Rubric to the 427 papers accepted and published at these venues over 2014 and 2015. Two people evaluated each paper using the Base Rubric for characterizing the paper. An individual person applied the other rubrics to characterize the norms of reporting, as appropriate for the paper type. Any discrepancies or questions were discussed between multiple reviewers to resolve. Results. We found that over 80% of papers accepted across all five venues had some form of empirical evaluation. Quantitative evaluation methods were the most frequently reported. Papers most frequently reported results on interventions around pedagogical techniques, curriculum, community, or tools. There was a split in papers that had some type of comparison between an intervention and some other dataset or baseline. Most papers reported related work, following the expectations for doing so in the SIGCSE and CER community. However, many papers were lacking properly reported research objectives, goals, research questions, or hypotheses; description of participants; study design; data collection; and threats to validity. These results align with prior surveys of the CER literature. Conclusions. CER authors are contributing empirical results to the literature; however, not all norms for reporting are met. We encourage authors to provide clear, labeled details about their work so readers can use the study methodologies and results for replications and meta-analyses. As our community grows, our reporting of CER should mature to help establish computing education theory to support the next generation of computing learners.

Light Diacritic Restoration to Disambiguate Homographs in Modern Arabic Texts

Diacritic restoration (also known as diacritization or vowelization) is the process of inserting the correct diacritical markings into a text. Modern Arabic is typically written without diacritics, e.g., newspapers. This lack of diacritical markings often causes ambiguity, and though natives are adept at resolving, there are times they may fail. Diacritic restoration is a classical problem in computer science. Still, as most of the works tackle the full (heavy) diacritization of text, we, however, are interested in diacritizing the text using a fewer number of diacritics. Studies have shown that a fully diacritized text is visually displeasing and slows down the reading. This article proposes a system to diacritize homographs using the least number of diacritics, thus the name “light.” There is a large class of words that fall under the homograph category, and we will be dealing with the class of words that share the spelling but not the meaning. With fewer diacritics, we do not expect any effect on reading speed, while eye strain is reduced. The system contains morphological analyzer and context similarities. The morphological analyzer is used to generate all word candidates for diacritics. Then, through a statistical approach and context similarities, we resolve the homographs. Experimentally, the system shows very promising results, and our best accuracy is 85.6%.

A genre-based analysis of questions and comments in Q&A sessions after conference paper presentations in computer science

Gender diversity in computer science at a large public r1 research university: reporting on a self-study.

With the number of jobs in computer occupations on the rise, there is a greater need for computer science (CS) graduates than ever. At the same time, most CS departments across the country are only seeing 25–30% of women students in their classes, meaning that we are failing to draw interest from a large portion of the population. In this work, we explore the gender gap in CS at Rutgers University–New Brunswick, a large public R1 research university, using three data sets that span thousands of students across six academic years. Specifically, we combine these data sets to study the gender gaps in four core CS courses and explore the correlation of several factors with retention and the impact of these factors on changes to the gender gap as students proceed through the CS courses toward completing the CS major. For example, we find that a significant percentage of women students taking the introductory CS1 course for majors do not intend to major in CS, which may be a contributing factor to a large increase in the gender gap immediately after CS1. This finding implies that part of the retention task is attracting these women students to further explore the major. Results from our study include both novel findings and findings that are consistent with known challenges for increasing gender diversity in CS. In both cases, we provide extensive quantitative data in support of the findings.

Designing for Student-Directedness: How K–12 Teachers Utilize Peers to Support Projects

Student-directed projects—projects in which students have individual control over what they create and how to create it—are a promising practice for supporting the development of conceptual understanding and personal interest in K–12 computer science classrooms. In this article, we explore a central (and perhaps counterintuitive) design principle identified by a group of K–12 computer science teachers who support student-directed projects in their classrooms: in order for students to develop their own ideas and determine how to pursue them, students must have opportunities to engage with other students’ work. In this qualitative study, we investigated the instructional practices of 25 K–12 teachers using a series of in-depth, semi-structured interviews to develop understandings of how they used peer work to support student-directed projects in their classrooms. Teachers described supporting their students in navigating three stages of project development: generating ideas, pursuing ideas, and presenting ideas. For each of these three stages, teachers considered multiple factors to encourage engagement with peer work in their classrooms, including the quality and completeness of shared work and the modes of interaction with the work. We discuss how this pedagogical approach offers students new relationships to their own learning, to their peers, and to their teachers and communicates important messages to students about their own competence and agency, potentially contributing to aims within computer science for broadening participation.

Creativity in CS1: A Literature Review

Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored. To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.

CATS: Customizable Abstractive Topic-based Summarization

Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user’s preference) remains unexplored. In this article, we present CATS, an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings’ transcripts datasets, AMI and International Computer Science Institute(ICSI) , results in merely a few hundred training documents.

Exploring students’ and lecturers’ views on collaboration and cooperation in computer science courses - a qualitative analysis

Factors affecting student educational choices regarding oer material in computer science, export citation format, share document.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

•  We're Hiring!
•  Help Center
• Computer Science
• Most Cited Papers
• Most Downloaded Papers
• Newest Papers
• Save to Library
• Last »
• Artificial Intelligence Follow Following
• Software Engineering Follow Following
• Computer Vision Follow Following
• Human Computer Interaction Follow Following
• Machine Learning Follow Following
• Data Mining Follow Following
• Computer Graphics Follow Following
• Distributed Computing Follow Following
• Computer Networks Follow Following
• Cloud Computing Follow Following

Enter the email address you signed up with and we'll email you a reset link.

• Academia.edu Publishing
•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Academia ©2024

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Perspective
• Published: 26 June 2023

GREENER principles for environmentally sustainable computational science

• Loïc Lannelongue   ORCID: orcid.org/0000-0002-9135-1345 1 , 2 , 3 , 4 ,
• Hans-Erik G. Aronson   ORCID: orcid.org/0000-0002-1702-1671 5 ,
• Alex Bateman 6 ,
• Ewan Birney 6 ,
• Talia Caplan   ORCID: orcid.org/0000-0001-8990-1435 7 ,
• Martin Juckes   ORCID: orcid.org/0000-0003-1770-2132 8 ,
• Johanna McEntyre 6 ,
• Andrew D. Morris 5 ,
• Gerry Reilly 5 &
• Michael Inouye 1 , 2 , 3 , 4 , 9 , 10 , 11  

Nature Computational Science volume  3 ,  pages 514–521 ( 2023 ) Cite this article

7485 Accesses

8 Citations

103 Altmetric

Metrics details

• Computational science
• Environmental impact
• Scientific community

The carbon footprint of scientific computing is substantial, but environmentally sustainable computational science (ESCS) is a nascent field with many opportunities to thrive. To realize the immense green opportunities and continued, yet sustainable, growth of computer science, we must take a coordinated approach to our current challenges, including greater awareness and transparency, improved estimation and wider reporting of environmental impacts. Here, we present a snapshot of where ESCS stands today and introduce the GREENER set of principles, as well as guidance for best practices moving forward.

Similar content being viewed by others

Improving microbial phylogeny with citizen science within a mass-market video game

Roman Sarrazin-Gendron, Parham Ghasemloo Gheidari, … Jérôme Waldispühl

Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations

Srinivas Niranj Chandrasekaran, Beth A. Cimini, … Anne E. Carpenter

Artificial intelligence and illusions of understanding in scientific research

Lisa Messeri & M. J. Crockett

Scientific research and development have transformed and immeasurably improved the human condition, whether by building instruments to unveil the mysteries of the universe, developing treatments to fight cancer or improving our understanding of the human genome. Yet, science can, and frequently does, impact the environment, and the magnitude of these impacts is not always well understood. Given the connection between climate change and human health, it is becoming increasingly apparent to biomedical researchers in particular, as well as their funders, that the environmental effects of research should be taken into account 1 , 2 , 3 , 4 , 5 .

Recent studies have begun to elucidate the environmental impacts of scientific research, with an initial focus on scientific conferences and experimental laboratories 6 . The 2019 Fall Meeting of the American Geophysical Union was estimated to emit 80,000 metric tonnes of CO 2 equivalent (tCO 2 e), equivalent to the average weekly emissions of the city of Edinburgh, UK 7 (CO 2 e, or CO 2 -equivalent, summarizes the global warming impacts of a range of greenhouse gases (GHGs) and is the standard metric for carbon footprints, although its accuracy is sometimes debated 8 ) The annual meeting of the Society for Neuroscience was estimated to emit 22,000 tCO 2 e, approximately the annual carbon footprint of 1,000 medium-sized laboratories 9 . The life-cycle impact (including construction and usage) of university buildings has been estimated at ~0.125 tCO 2 e m −2  yr −1 (ref. 10 ), and the yearly carbon footprint of a typical life-science laboratory at ~20 tCO 2 e (ref. 9 ). The Laboratory Efficiency Assessment Framework (LEAF) is a widely adopted standard to monitor and reduce the carbon footprint of laboratory-based research 11 . Other recent frameworks can help to raise awareness: GES 1point5 12 provides an open-source tool to estimate the carbon footprint of research laboratories and covers buildings, procurement, commuting and travel, and the Environmental Responsibility 5-R Framework provides guidelines for ecologically conscious research 13 .

With the increasing scale of high-performance and cloud computing, the computational sciences are susceptible to having silent and unintended environmental impacts. The sector of information and communication technologies (ICT) was responsible for between 1.8% and 2.8% of global GHG emissions in 2020 14 —more than aviation (1.9% 15 )—and, if unchecked, the ICT carbon footprint could grow exponentially in coming years 14 . Although the environmental impact of experimental ‘wet’ laboratories is more immediately obvious, with their large pieces of equipment and high plastic and reagent usage, the impact of algorithms is less clear and often underestimated. The risks of seeking performance at any cost and the importance of considering energy usage and sustainability when developing new hardware for high-performance computing (HPC) was raised as early as 2007 16 . Since then, continuous improvements have been made by developing new hardware, building lower-energy data centers and implementing more efficient HPC systems 17 , 18 . However, it is only in the past five years that these concerns have reached HPC users, in particular researchers. Notably, the field of artificial intelligence (AI) has first taken note of its environmental impacts, in particular those of the very large language models developed 19 , 20 , 21 , 22 , 23 . It is unclear, however, to what extent this has led the field towards more sustainable research practices. A small number of studies have also been performed in other fields, including bioinformatics 24 , astronomy and astrophysics 25 , 26 , 27 , 28 , particle physics 29 , neuroscience 30 and computational social sciences 31 . Health data science is starting to address the subject, but a recent systematic review found only 25 publications in the field over the past 12 years 32 . In addition to the environmental effects of electricity usage, manufacturing and disposal of hardware, there are also concerns around data centers’ water usage and land footprint 33 . Notably, computational science, in particular AI, has the potential to help fight climate change, for example, by improving the efficiency of wind farms, by facilitating low-carbon urban mobility and by better understanding and anticipating severe weather events 34 .

In this Perspective we highlight the nascent field of environmentally sustainable computational science (ESCS)—what we have learned from the research so far, and what scientists can do to mitigate their environmental impacts. In doing so, we present GREENER (Governance, Responsibility, Estimation, Energy and embodied impacts, New collaborations, Education and Research; Fig. 1 ), a set of principles for how the computational science community could lead the way in sustainable research practices, maximizing computational science’s benefit to both humanity and the environment.

The GREENER principles enable cultural change (blue arrows), which in turn facilitates their implementation (green arrows) and triggers a virtuous circle.

Environmental impacts of the computational sciences

The past three years have seen increased concerns regarding the carbon footprint of computations, and only recently have tools 21 , 35 , 36 , 37 and guidelines 38 been widely available to computational scientists to allow them to estimate their carbon footprint and be more environmentally sustainable.

Most calculators that estimate the carbon footprint of computations are targeted at machine learning tasks and so are primarily suited to Python pipelines, graphics processing units (GPUs) and/or cloud computing 36 , 37 , 39 , 40 . Python libraries have the benefit of integrating well into machine learning pipelines or online calculators for cloud GPUs 21 , 41 . Recently, a flexible online tool, the Green Algorithms calculator 35 , enabled the estimation of the carbon footprint for nearly any computational task, empowering sustainability metrics across fields, hardware, computing platforms and locations.

Some publications, such as ref. 38 , have listed simple actions that computational scientists can take regarding their environmental impact, including estimating the carbon footprint of running algorithms, both a posteriori to acknowledge the impact of a project and before starting as part of a cost–benefit analysis. A 2020 report from The Royal Society formalizes this with the notion of ‘energy proportionality’, meaning the environmental impacts of an innovation must be outweighed by its environmental or societal benefits 34 . It is also important to minimize electronic waste by keeping devices for longer and using second-hand hardware when possible. A 2021 report by the World Health Organization 42 warns of the dramatic effect of e-waste on population health, particularly children. The unregulated informal recycling industry, which handles more than 80% of the 53 million tonnes of e-waste, causes a high level of water, soil and air pollution, often in low- and middle-income countries 43 . The up to 56 million informal waste workers are also exposed to hazardous chemicals such as heavy metals and persistent organic pollutants 42 . Scientists can also choose energy-efficient hardware and computing facilities, while favoring those powered by green energy. Writing efficient code can substantially reduce the carbon footprint as well, and this can be done alongside making hardware requirements and carbon footprints clear when releasing new software. The Green Software Foundation ( https://greensoftware.foundation ) promotes carbon-aware coding to reduce the operational carbon footprint of the softwares used in all aspects of society. There is, however, a rebound effect to making algorithms and hardware more efficient: instead of reducing computing usage, increased efficiency encourages more analyses to be performed, which leads to a revaluation of the cost–benefit but often results in increased carbon footprints. The rebound effect is a key example of why research practice should adapt to technological advances so that they lead to carbon footprint reductions.

GREENER computational science

ESCS is an emerging field, but one that is of rapidly increasing importance given the climate crisis. In the following, our proposed set of principles (Fig. 1 ) outlines the main axes where progress is needed, where opportunities lie and where we believe efforts should be concentrated.

Governance and responsibility

Everyone involved in computational science has a role to play in making the field more sustainable, and many do already, from grassroots movements to large institutions. Individual and institutional responsibility is a necessary step to ensure transparency and reduction of GHG emission. Here we highlight key stakeholders alongside existing initiatives and future opportunities for involvement.

Grassroots initiatives led by graduate students, early career researchers and laboratory technicians have shown great success in tackling the carbon footprint of laboratory work, including Green Labs Netherlands 44 , the Nottingham Technical Sustainability Working Group or the Digital Humanities Climate Coalition 45 . International coalitions such as the Sustainable Research (SuRe) Symposium, initially set up for wet laboratories, have started to address the impact of computing as well. IT teams in HPC centers are naturally key, both in terms of training and ensuring that the appropriate information is logged so that scientists can follow the carbon footprints of their work. Principal investigators can encourage their teams to think about this issue and provide access to suitable training when needed.

Simultaneously, top–down approaches are needed, with funding bodies and journals occupying key positions in both incentivizing carbon-footprint reduction and in promoting transparency. Funding bodies can directly influence the researchers they fund and those applying for funding via their funding policies. They can require estimates of carbon footprints to be included in funding applications as part of ‘environmental impacts statements’. Many funding bodies include sustainability in their guidelines already; see, for example, the UK’s NIHR carbon reduction guidelines 1 , the brief mention of the environment in UKRI’s terms and conditions 46 , and the Wellcome Trust’s carbon-offsetting travel policy 47 .

Although these are important first steps, bolder action is needed to meet the urgency of climate change. For example, UKRI’s digital research infrastructure scoping project 48 , which seeks to provide a roadmap to net zero for its digital infrastructure, sends a clear message that sustainable research includes minimizing the GHG emissions from computation. The project not only raises awareness but will hopefully result in reductions in GHG emissions.

Large research institutes are key to managing and expanding centralized data infrastructures and trusted research environments (TREs). For example, EMBL’s European Bioinformatics Institute manages more than 40 data resources 49 , including AlphaFold DB 50 , which contains over 200,000,000 predicted protein structures that can be searched, browsed and retrieved according to the FAIR principles (findable, accessible, interoperable, reusable) 51 . As a consequence, researchers do not need to run the carbon-intensive AlphaFold algorithm for themselves and instead can just query the database. AlphaFold DB was queried programmatically over 700 million times and the web page was accessed 2.4 million times between August 2021 and October 2022. Institutions also have a role in making procurement decisions carefully, taking into account both the manufacturing and operational footprint of hardware purchases. This is critical, as the lifetime footprint of a computational facility is largely determined by the date it is purchased. Facilities could also better balance investment decisions, with a focus on attracting staff based on sustainable and efficient working environments, rather than high-powered hardware 52 .

However, increases in the efficiencies of digital technology alone are unlikely to prove sufficient in ensuring sustainable resource use 53 . Alongside these investments, funding bodies should support a shift towards more positive, inclusive and green research cultures, recognizing that more data or bigger models do not always translate into greater insights and that a ‘fit for purpose’ approach can ultimately be more efficient. Organizations such as Health Data Research UK and the UK Health Data Research Alliance have a key convening role in ensuring that awareness is raised around the climate impact of both infrastructure investment and computational methods.

Journals may incentivize authors to acknowledge and indeed estimate the carbon footprint of the work presented. Some authors already do this voluntarily (for example, refs. 54 , 55 , 56 , 57 , 58 , 59 ), mostly in bioinformatics and machine learning so far, but there is potential to expand it to other areas of computational science. In some instances, showing that a new tool is greener can be an argument in support of a new method 60 .

International societies in charge of organizing annual conferences may help scientists reduce the carbon footprint of presenting their work by offering hybrid options. The COVID-19 pandemic boosted virtual and hybrid meetings, which have a lower carbon footprint while increasing access and diversity 7 , 61 . Burtscher and colleagues found that running the annual meeting of the European Astronomical Society online emitted >3,000-fold less CO 2 e than the in-person meeting (0.582 tCO 2 e compared to 1,855 tCO 2 e) 25 . Institutions are starting to tackle this; for example, the University of Cambridge has released new travel guidelines encouraging virtual meetings whenever feasible and restricting flights to essential travel, while also acknowledging that different career stages have different needs 62 .

Industry partners will also need to be part of the discussion. Acknowledging and reducing computing environmental impact comes with added challenges in industry, such as shareholder interests and/or public relations. While the EU has backed some initiatives helping ICT-reliant companies to address their carbon footprint, such as ICTfootprint.eu, other major stakeholders have expressed skepticism regarding the environmental issues of machine learning models 63 , 64 . Although challenging, tech industry engagement and inclusion is nevertheless essential for tackling GHG emissions.

Estimate and report the energy consumption of algorithms

Estimating and monitoring the carbon footprint of computations is an essential step towards sustainable research as it identifies inefficiencies and opportunities for improvement. User-level metrics are crucial to understanding environmental impacts and promoting personal responsibility. In some HPC situations, particularly in academia, the financial cost of running computations is negligible and scientists may have the impression of unlimited and inconsequential computing capacity. Quantifying the carbon footprint of individual projects helps raise awareness of the true costs of research.

Although progress has been made in estimating energy usage and carbon footprints over the past few years, there are still barriers that prevent the routine estimation of environmental impacts. From task-agnostic, general-purpose calculators 35 and task-specific packages 36 , 37 , 65 to server-side softwares 66 , 67 , each estimation tool is a trade-off between ease of use and accuracy. A recent primer 68 discusses these different options in more detail and provides recommendations as to which approach fits a particular need.

Regardless of the calculator used, for these tools to work effectively and for scientists to have an accurate representation of their energy consumption, it is important to understand the power management for different components. For example, the power usage of processing cores such as central processing units (CPUs) and GPUs is not a readily available metric; instead, thermal design power (meaning, how much heat the chip can be expected to dissipate in a normal setting) is used. Although an acceptable approximation, it has also been shown to substantially underestimate power usage in some situations 69 . The efficiency of data centers is measured by the power usage effectiveness (PUE), which quantifies how much energy is needed for non-computing tasks, mainly cooling (efficient data centers have PUEs close to 1). This metric is widely used, with large cloud providers reporting low PUEs (for example, 1.11 for Google 70 compared to a global average of 1.57 71 ), but discrepancies in how it is calculated can limit PUE interpretation and thus its impact 72 , 73 , 74 . A standard from the International Organization for Standardization is trying to address this 75 . Unfortunately, the PUE of a particular data center, whether cloud or institutional, is rarely publicly documented. Thus, an important step is the data science and infrastructure community making both hardware and data centers’ energy consumption metrics available to their users and the public. Ultimately, tackling unnecessary carbon footprints will require transparency 34 .

Tackling energy and embodied impacts through new collaborations

Minimizing carbon intensity (meaning the carbon footprint of producing electricity) is one of the most immediately impactful ways to reduce GHG emissions. Carbon intensities depend largely on geographical location, with up to three orders of magnitude between the top and bottom performing high-income countries in terms of low carbon energies (from 0.10 gCO 2 e kWh −1 in Iceland to 770 gCO 2 e kWh −1 in Australia 76 ). Changing the carbon intensity of a local state or national government is nearly always impractical as it would necessitate protracted campaigns to change energy policies. An alternative is to relocate computations to low-carbon settings and countries, but, depending on the type of facility or the sensitivity of the data, this may not always be possible. New inter-institutional cooperation may open up opportunities to enable access to low-carbon data centers in real time.

It is, however, essential to recognize and account for inequalities between countries in terms of access to green energy sources. International cooperation is key to providing scientists from low- and middle-income countries (LMICs), who frequently only have high-carbon-intensity options available to them, access to low-carbon computing infrastructures for their work. In the longer term, international partnerships between organizations and nations can help build low-carbon computing capacity in LMICs.

Furthermore, the footprint of user devices should not be forgotten. In one estimate, the energy footprint of streaming a video to a laptop is mainly on the laptop (72%), with 23% used in transmission and a mere 5% at the data center 77 . Zero clients (user devices with no compute or storage capacity) can be used in some research use cases and drastically reduce the client-side footprint 78 .

It can be tempting to reduce the environmental impacts of computing to electricity needs, as these are the easiest ones to estimate. However, water usage, ecological impacts and embodied carbon footprints from manufacturing should also be addressed. For example, for personal hardware, such as laptops, 70–80% of the life-cycle impact of these devices comes from manufacturing only 79 , as it involves mining raw materials and assembling the different components, which require water and energy. Moreover, manufacturing often takes place in countries that have a higher carbon intensity for power generation and a slower transition to zero-carbon power 80 . Currently, hardware renewal policies, either for work computers or servers in data centers, are often closely dependent on warranties and financial costs, with environmental costs rarely considered. For hardware used in data centers, regular updates may be both financially and environmentally friendly, as efficiency gains may offset manufacturing impacts. Estimating these environmental impacts will allow HPC teams to know for sure. Reconditioned and remanufactured laptops and servers are available, but growth of this sector is currently limited by negative consumer perception 81 . Major suppliers of hardware are making substantial commitments, such as 100% renewable energy supply by 2030 82 or net zero by 2050 83 .

Another key consideration is data storage. Scientific datasets are now measured in petabytes (PB). In genomics, the popular UK Biobank cohort 84 is expected to reach 15 PB by 2025 85 , and the first image of a black hole required the collection of 5 PB of data 86 . The carbon footprint of storing data depends on numerous factors, but based on some manufacturers’ estimations, the order of magnitude of the life-cycle footprint of storing 1 TB of data for a year is ~10 kg CO 2 e (refs. 87 , 88 ). This issue is exacerbated by the duplication of such datasets in order for each institution, and sometimes each research group, to have a copy. Centralized and collaborative computing resources (such as TREs) holding both data and computing hardware may help alleviate redundant resources. TRE efforts in the UK span both health (for example, NHS Digital 89 ) and administrative data (for example, the SAIL databank on the UK Secure Research Platform 90 and the Office for National Statistics Secure Research Service 91 ). Large (hyperscale) data centers are expected to be more energy-efficient 92 , but they may also encourage unnecessary increases in the scale of computing (rebound effect).

The importance of dedicated education and research efforts for ESCS

Education is essential to raise awareness with different stakeholders. In lieu of incorporating some aspects into more formal undergraduate programs, integrating sustainability into computational training courses is a tangible first step toward reducing carbon footprints. An example is the ‘Green Computing’ Workshop on Education at the 2022 conference on Intelligent Systems for Molecular Biology.

Investing in research that will catalyze innovation in the field of ESCS is a crucial role for funders and institutions to play. Although global data centers’ workloads have increased more than sixfold between 2010 and 2018, their total electricity usage has been approximately stable due to the use of power-efficient hardware 93 , but environmentally sustainable investments will be needed to perpetuate this trend. Initiatives like Wellcome’s Research Sustainability project 94 , which look to highlight key gaps where investment could deliver the next generation of ESCS tools and technology, are key to ensuring that growth in energy demand beyond current efficiency trends can be managed in a sustainable way. Similarly, the UKRI Data and Analytics Research Environments UK program (DARE UK) needs to ensure that sustainability is a key evaluation criterion for funding and infrastructure investments for the next generation of TREs.

Recent studies found that the most widely used programming languages in research, such as R and Python 95 , tend to be the least energy-efficient ones 96 , 97 , and, although it is unlikely that forcing the community to switch to more efficient languages would benefit the environment in the short term (due to inefficient coding for example), this highlights the importance of having trained research software engineers within research groups to ensure that the algorithms used are efficiently implemented. There is also scope to use current tools more efficiently by better understanding and monitoring how coding choices impact carbon footprints. Algorithms also come with high memory requirements, sometimes using more energy than processors 98 . Unfortunately, memory power usage remains poorly optimized, as speed of access is almost always favored over energy efficiency 99 . Providing users and software engineers with the flexibility to opt for energy efficiency would present an opportunity for a reduction in GHG emissions 100 , 101 .

Cultural change

In parallel to the technological reductions in energy usage and carbon footprints, research practices will also need to change to avoid rebound effects 38 . Similar to the aviation industry, there is a tendency to count on technology to solve sustainability concerns without having to change usage 102 (that is, waiting on computing to become zero-carbon rather than acting on how we use it). Cultural change in the computing community to reconsider how we think about computing costs will be necessary. Research strategies at all levels will need to consider environmental impacts and corresponding approaches to carbon footprint minimization. The upcoming extension of the LEAF standard for computational laboratories will provide researchers with tangible tools to do so. Day to day, there is a need to solve trade-offs between the speed of computation, accuracy and GHG emissions, keeping in mind the goal of GHG reduction. These changes in scientific practices are challenging, but, importantly, there are synergies between open computational science and green computing 103 . For example, making code, data and models FAIR so that other scientists avoid unnecessary computations can increase the reach and impact of a project. FAIR practices can result in highly efficient code implementations, reduce the need to retrain models, and reduce unnecessary data generation/storage, thus reducing the overall carbon footprint. As a result, green computing and FAIR practices may both stimulate innovation and reduce financial costs.

Moreover, computational science has downstream effects on carbon footprints in other areas. In the biomedical sciences, developments in machine learning and computer vision impact the speed and scale of medical imaging processing. Discoveries in health data science make their way to clinicians and patients through, for example, connected devices. In each of these cases and many others, environmental impacts propagate through the whole digital health sector 32 . Yet, here too synergies exist. In many cases, such as telemedicine, there may be a net benefit in terms of both carbon and patient care, provided that all impacts have been carefully accounted for. These questions are beginning to be tackled in medicine, such as assessments of the environmental impact of telehealth 104 or studies into ways to sustainably handle large volumes of medical imaging data 105 . For the latter, NHS Digital (the UK’s national provider of information, data and IT systems for health and social care) has released guidelines to this effect 106 . Outside the biomedical field, there are immense but, so far, unrealized opportunities for similar efforts.

The computational sciences have an opportunity to lead the way in sustainability, which may be achieved through the GREENER principles for ESCS (Fig. 1 ): Governance, Responsibility, Estimation, Energy and embodied impacts, New collaborations, Education and Research. This will require more transparency on environmental impacts. Although some tools already exist to estimate carbon footprints, more specialized ones will be needed alongside a clearer understanding of the carbon footprint of hardware and facilities, as well as more systematic monitoring and acknowledgment of carbon footprints. Measurement is a first step, followed by a reduction in GHG emissions. This can be achieved with better training and sensible policies for renewing hardware and storing data. Cooperation, open science and equitable access to low-carbon computing facilities will also be crucial 107 . Computing practices will need to adapt to include carbon footprints in cost–benefit analyses, as well as consider the environmental impacts of downstream applications. The development of sustainable solutions will need particularly careful consideration, as they frequently have the least benefit for populations, often in LMICs, who suffer the most from climate change 22 , 108 . All stakeholders have a role to play, from funding bodies, journals and institutions to HPC teams and early career researchers. There is now a window of time and an immense opportunity to transform computational science into an exemplar of broad societal impact and sustainability.

NIHR Carbon Reduction Guidelines (National Institute for Health and Care Research, 2019); https://www.nihr.ac.uk/documents/nihr-carbon-reduction-guidelines/21685

NHS Becomes the World’s First National Health System to Commit to Become ‘Carbon Net Zero’, Backed by Clear Deliverables and Milestones (NHS England, 2020); https://www.england.nhs.uk/2020/10/nhs-becomes-the-worlds-national-health-system-to-commit-to-become-carbon-net-zero-backed-by-clear-deliverables-and-milestones/

Climate and COVID-19: converging crises. Lancet 397 , 71 (2021).

Marazziti, D. et al. Climate change, environment pollution, COVID-19 pandemic and mental health. Sci. Total Environ. 773 , 145182 (2021).

Article   Google Scholar  

Wellcome Commissions Report on Science’s Environmental Impact (Wellcome, 2022); https://wellcome.org/news/wellcome-commissions-report-sciences-environmental-impact

Towards Climate Sustainability of the Academic System in Europe and Beyond (ALLEA, 2022); https://doi.org/10.26356/climate-sust-acad

Klöwer, M., Hopkins, D., Allen, M. & Higham, J. An analysis of ways to decarbonize conference travel after COVID-19. Nature 583 , 356–359 (2020).

Allen, M. R. et al. A solution to the misrepresentations of CO 2 -equivalent emissions of short-lived climate pollutants under ambitious mitigation. npj Clim. Atmos. Sci. 1 , 16 (2018).

Nathans, J. & Sterling, P. How scientists can reduce their carbon footprint. eLife 5 , e15928 (2016).

Helmers, E., Chang, C. C. & Dauwels, J. Carbon footprinting of universities worldwide part II: first quantification of complete embodied impacts of two campuses in Germany and Singapore. Sustainability 14 , 3865 (2022).

Marshall-Cook, J. & Farley, M. Sustainable Science and the Laboratory Efficiency Assessment Framework ( LEAF ) (UCL, 2023).

Mariette, J. et al. An open-source tool to assess the carbon footprint of research. Environ. Res. Infrastruct. Sustain. 2 , 035008 (2022).

Murray, D. S. et al. The environmental responsibility framework: a toolbox for recognizing and promoting ecologically conscious research. Earth’s Future 11 , e2022EF002964 (2023).

Freitag, C. et al. The real climate and transformative impact of ICT: a critique of estimates, trends and regulations. Patterns 2 , 100340 (2021).

Ritchie, H. Climate change and flying: what share of global CO 2 emissions come from aviation? Our World in Data (22 October 2022); https://ourworldindata.org/co2-emissions-from-aviation

Feng, W. & Cameron, K. The Green500 list: encouraging sustainable supercomputing. Computer 40 , 50–55 (2007).

Garg, S. K., Yeo, C. S., Anandasivam, A. & Buyya, R. Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers. J. Parallel Distrib. Comput. 71 , 732–749 (2011).

Article   MATH   Google Scholar  

Katal, A., Dahiya, S. & Choudhury, T. Energy efficiency in cloud computing data centers: a survey on software technologies. Clust. Comput. https://doi.org/10.1007/s10586-022-03713-0 (2022).

Strubell, E., Ganesh, A. & McCallum, A. Energy and policy considerations for deep learning in NLP. In Proc. 57th Annual Meeting of the Association for Computational Linguistics 3645–3650 (Association for Computational Linguistics, 2019); https://doi.org/10.18653/v1/P19-1355

Schwartz, R., Dodge, J., Smith, N. A. & Etzioni, O. Green AI. Preprint at https://arxiv.org/abs/1907.10597 (2019).

Lacoste, A., Luccioni, A., Schmidt, V. & Dandres, T. Quantifying the carbon emissions of machine learning. Preprint at https://arxiv.org/abs/1910.09700 (2019).

Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. On the dangers of stochastic parrots: can language models be too big? In Proc. 2021 ACM Conference on Fairness , Accountability and Transparency 610–623 (Association for Computing Machinery, 2021); https://doi.org/10.1145/3442188.3445922

Memmel, E., Menzen, C., Schuurmans, J., Wesel, F. & Batselier, K. Towards Green AI with tensor networks—sustainability and innovation enabled by efficient algorithms. Preprint at https://doi.org/10.48550/arXiv.2205.12961 (2022).

Grealey, J. et al. The carbon footprint of bioinformatics. Mol. Biol. Evol. 39 , msac034 (2022).

Burtscher, L. et al. The carbon footprint of large astronomy meetings. Nat. Astron. 4 , 823–825 (2020).

Jahnke, K. et al. An astronomical institute’s perspective on meeting the challenges of the climate crisis. Nat. Astron. 4 , 812–815 (2020).

Stevens, A. R. H., Bellstedt, S., Elahi, P. J. & Murphy, M. T. The imperative to reduce carbon emissions in astronomy. Nat. Astron. 4 , 843–851 (2020).

Portegies Zwart, S. The ecological impact of high-performance computing in astrophysics. Nat. Astron. 4 , 819–822 (2020).

Bloom, K. et al. Climate impacts of particle physics. Preprint at https://arxiv.org/abs/2203.12389 (2022).

Aron, A. R. et al. How can neuroscientists respond to the climate emergency? Neuron 106 , 17–20 (2020).

Leslie, D. Don’t ‘ Research Fast and Break Things ’: on the Ethics of Computational Social Science (Zenodo, 2022); https://doi.org/10.5281/zenodo.6635569

Samuel, G. & Lucassen, A. M. The environmental sustainability of data-driven health research: a scoping review. Digit. Health 8 , 205520762211112 (2022).

Al Kez, D., Foley, A. M., Laverty, D., Del Rio, D. F. & Sovacool, B. Exploring the sustainability challenges facing digitalization and internet data centers. J. Clean. Prod. 371 , 133633 (2022).

Digital Technology and the Planet—Harnessing Computing to Achieve Net Zero (The Royal Society, 2020); https://royalsociety.org/topics-policy/projects/digital-technology-and-the-planet/

Lannelongue, L., Grealey, J. & Inouye, M. Green algorithms: quantifying the carbon footprint of computation. Adv. Sci. 8 , 2100707 (2021).

Henderson, P. et al. Towards the systematic reporting of the energy and carbon footprints of machine learning. J. Mach. Learn. Res. 21 , 10039–10081 (2020).

MathSciNet   Google Scholar  

Anthony, L. F. W., Kanding, B. & Selvan, R. Carbontracker: tracking and predicting the carbon footprint of training deep learning models. Preprint at https://arxiv.org/abs/2007.03051 (2020).

Lannelongue, L., Grealey, J., Bateman, A. & Inouye, M. Ten simple rules to make your computing more environmentally sustainable. PLoS Comput. Biol. 17 , e1009324 (2021).

Valeye, F. Tracarbon. GitHub https://github.com/fvaleye/tracarbon (2022).

Trébaol, T. CUMULATOR—a Tool to Quantify and Report the Carbon Footprint of Machine Learning Computations and Communication in Academia and Healthcare (École Polytechnique Fédérale de Lausanne, 2020).

Cloud Carbon Footprint —An open source tool to measure and analyze cloud carbon emissions. https://www.cloudcarbonfootprint.org/ (2023).

Children and Digital Dumpsites: E-Waste Exposure and Child Health (World Health Organization, 2021); https://apps.who.int/iris/handle/10665/341718

Sepúlveda, A. et al. A review of the environmental fate and effects of hazardous substances released from electrical and electronic equipments during recycling: examples from China and India. Environ. Impact Assess. Rev. 30 , 28–41 (2010).

Franssen, T. & Johnson, H. The Implementation of LEAF at Public Research Organisations in the Biomedical Sciences: a Report on Organisational Dynamics (Zenodo, 2021); https://doi.org/10.5281/ZENODO.5771609

DHCC Information, Measurement and Practice Action Group. A Researcher Guide to Writing a Climate Justice Oriented Data Management Plan (Zenodo, 2022); https://doi.org/10.5281/ZENODO.6451499

UKRI. UKRI Grant Terms and Conditions (UKRI, 2022); https://www.ukri.org/wp-content/uploads/2022/04/UKRI-050422-FullEconomicCostingGrantTermsConditionsGuidance-Apr2022.pdf

Carbon Offset Policy for Travel—Grant Funding (Wellcome, 2021); https://wellcome.org/grant-funding/guidance/carbon-offset-policy-travel

Juckes, M., Pascoe, C., Woodward, L., Vanderbauwhede, W. & Weiland, M. Interim Report: Complexity, Challenges and Opportunities for Carbon Neutral Digital Research (Zenodo, 2022); https://zenodo.org/record/7016952

Thakur, M. et al. EMBL’s European Bioinformatics Institute (EMBL-EBI) in 2022. Nucleic Acids Res 51 , D9–D17 (2022).

Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50 , D439–D444 (2022).

Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3 , 160018 (2016).

Bichsel, J. Research Computing : The Enabling Role of Information Technology (Educause, 2012); https://library.educause.edu/resources/2012/11/research-computing-the-enabling-role-of-information-technology

Creutzig, F. et al. Digitalization and the Anthropocene. Annu. Rev. Environ. Resour. 47 , 479–509 (2022).

Yang, L. & Chen, J. A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions. Microbiome 10 , 130 (2022).

Qin, Y. et al. Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort. Nat. Genet. 54 , 134–142 (2022).

Lannelongue, L. & Inouye, M. Inference Mechanisms and Prediction of Protein-Protein Interactions . Preprint at http://biorxiv.org/lookup/doi/10.1101/2022.02.07.479382 (2022).

Dubois, F. The Vehicle Routing Problem for Flash Floods Relief Operations (Univ. Paul Sabatier, 2022).

Thiele, L., Cranmer, M., Coulton, W., Ho, S. & Spergel, D. N. Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks. Preprint at https://arxiv.org/abs/2203.00026 (2022).

Armstrong, G. et al. Efficient computation of Faith’s phylogenetic diversity with applications in characterizing microbiomes. Genome Res. 31 , 2131–2137 (2021).

Mbatchou, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat. Genet. 53 , 1097–1103 (2021).

Estien, C. O., Myron, E. B., Oldfield, C. A. & Alwin, A. & Ecological Society of America Student Section Virtual scientific conferences: benefits and how to support underrepresented students. Bull. Ecol. Soc. Am. 102 , e01859 (2021).

University of Cambridge. Guidelines for Sustainable Business Travel (Univ. Cambridge, 2022); https://www.environment.admin.cam.ac.uk/files/guidelines_for_sustainable_business_travel_approved.pdf

Patterson, D. et al. Carbon emissions and large neural network training. Preprint at https://arxiv.org/abs/2104.10350 (2021).

Patterson, D. et al. The carbon footprint of machine learning training will plateau, then shrink. Computer 55 , 18–28 (2022).

Neuroimaging Pipeline Carbon Tracker Toolboxes (OHBM SEA-SIG, 2023); https://ohbm-environment.org/carbon-tracker-toolboxes/

Lannelongue, L. Green Algorithms for High Performance Computing (GitHub, 2022); https://github.com/Llannelongue/GreenAlgorithms4HPC

Carbon Footprint Reporting—Customer Carbon Footprint Tool (Amazon Web Services, 2023); https://aws.amazon.com/aws-cost-management/aws-customer-carbon-footprint-tool/

Lannelongue, L. & Inouye, M. Carbon footprint estimation for computational research. Nat. Rev. Methods Prim. 3 , 9 (2023).

Cutress, I. Why Intel Processors Draw More Power Than Expected : TDP and Turbo Explained (Anandtech, 2018); https://www.anandtech.com/show/13544/why-intel-processors-draw-more-power-than-expected-tdp-turbo

Efficiency. Google Data Centers https://www.google.com/about/datacenters/efficiency/

Uptime Institute Releases 2021 Global Data Center Survey (Facility Executive, 2021); https://facilityexecutive.com/2021/09/uptime-institute-releases-2021-global-data-center-survey/

Zoie, R. C., Mihaela, R. D. & Alexandru, S. An analysis of the power usage effectiveness metric in data centers. In Proc. 2017 5th International Symposium on Electrical and Electronics Engineering ( ISEEE ) 1–6 (IEEE, 2017); https://doi.org/10.1109/ISEEE.2017.8170650

Yuventi, J. & Mehdizadeh, R. A critical analysis of power usage effectiveness and its use in communicating data center energy consumption. Energy Build. 64 , 90–94 (2013).

Avelar, V., Azevedo, D. & French, A. (eds) PUE: A Comprehensive Examination of the Metric White Paper No. 49 (Green Grid, 2012).

Power Usage Effectiveness (PUE) (ISO/IEC); https://www.iso.org/obp/ui/#iso:std:iso-iec:30134:-2:ed-1:v1:en

2022 Country Specific Electricity Grid Greenhouse Gas Emission Factors (Carbon Footprint, 2023); https://www.carbonfootprint.com/docs/2023_02_emissions_factors_sources_for_2022_electricity_v10.pdf

Kamiya, G. The Carbon Footprint of Streaming Video: Fact-Checking the Headlines—Analysis (IEA, 2020); https://www.iea.org/commentaries/the-carbon-footprint-of-streaming-video-fact-checking-the-headlines

Rot, A., Chrobak, P. & Sobinska, M. Optimisation of the use of IT infrastructure resources in an institution of higher education: a case study. In Proc. 2019 9th International Conference on Advanced Computer Information Technologies ( ACIT ) 171–174 (IEEE, 2019); https://doi.org/10.1109/ACITT.2019.8780018

Clément, L.-P. P.-V. P., Jacquemotte, Q. E. S. & Hilty, L. M. Sources of variation in life cycle assessments of smartphones and tablet computers. Environ. Impact Assess. Rev. 84 , 106416 (2020).

Kamal, K. Y. The silicon age: trends in semiconductor devices industry. JESTR 15 , 110–115 (2022).

Gåvertsson, I., Milios, L. & Dalhammar, C. Quality labelling for re-used ICT equipment to support consumer choice in the circular economy. J. Consum. Policy 43 , 353–377 (2020).

Intel Corporate Responsibility Report 2021–2022 (Intel, 2022); https://csrreportbuilder.intel.com/pdfbuilder/pdfs/CSR-2021-22-Full-Report.pdf

TSMC Task Force on Climate-related Financial Disclosures (TSMC, 2020); https://esg.tsmc.com/download/file/TSMC_TCFD_Report_E.pdf

Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562 , 203–209 (2018).

UK Biobank Creates Cloud-Based Health Data Analysis Platform to Unleash the Imaginations of the World’s Best Scientific Minds (UK Biobank, 2020); https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/news/uk-biobank-creates-cloud-based-health-data-analysis-platform-to-unleash-the-imaginations-of-the-world-s-best-scientific-minds

Jackson, K. A picture is worth a petabyte of data. Science Node (5 June 2019).

Nguyen, B. H. et al. Architecting datacenters for sustainability: greener data storage using synthetic DNA. In Proc. Electronics Goes Green 2020 (ed. Schneider-Ramelow, F.) 105 (Fraunhofer, 2020).

Seagate Product Sustainability (Seagate, 2023); https://www.seagate.com/gb/en/global-citizenship/product-sustainability/

Madden, S. & Pollard, C. Principles and Best Practices for Trusted Research Environments (NHS England, 2021); https://transform.england.nhs.uk/blogs/principles-and-practice-for-trusted-research-environments/

Jones, K. H., Ford, D. V., Thompson, S. & Lyons, R. A profile of the SAIL Databank on the UK secure research platform. Int. J. Popul. Data Sci. 4 , 1134 (2020).

Google Scholar  

About the Secure Research Service (Office for National Statistics); https://www.ons.gov.uk/aboutus/whatwedo/statistics/requestingstatistics/secureresearchservice/aboutthesecureresearchservice

Shehabi, A. et al. United States Data Center Energy Usage Report Report no. LBNL-1005775, 1372902 (Office of Scientific and Technical Information, 2016); http://www.osti.gov/servlets/purl/1372902/

Masanet, E., Shehabi, A., Lei, N., Smith, S. & Koomey, J. Recalibrating global data center energy-use estimates. Science 367 , 984–986 (2020).

Caplan, T. Help Us Advance Environmentally Sustainable Research (Wellcome, 2022); https://medium.com/wellcome-data/help-us-advance-environmentally-sustainable-research-3c11fe2a8298

Choueiry, G. Programming Languages Popularity in 12,086 Research Papers (Quantifying Health, 2023); https://quantifyinghealth.com/programming-languages-popularity-in-research/

Pereira, R. et al. Ranking programming languages by energy efficiency. Sci. Comput. Program. 205 , 102609 (2021).

Lin, Y. & Danielsson, J. Choosing a Numerical Programming Language for Economic Research: Julia, MATLAB, Python or R (Centre for Economic Policy Research, 2022); https://cepr.org/voxeu/columns/choosing-numerical-programming-language-economic-research-julia-matlab-python-or-r

Appuswamy, R., Olma, M. & Ailamaki, A. Scaling the memory power wall with DRAM-aware data management. In Proc. 11th International Workshop on Data Management on New Hardware 1–9 (ACM, 2015); https://doi.org/10.1145/2771937.2771947

Guo, B., Yu, J., Yang, D., Leng, H. & Liao, B. Energy-efficient database systems: a systematic survey. ACM Comput. Surv. 55 , 111 (2022).

Karyakin, A. & Salem, K. An analysis of memory power consumption in database systems. In Proc. 13th International Workshop on Data Management on New Hardware—DAMON ’ 17 1–9 (ACM Press, 2017); https://doi.org/10.1145/3076113.3076117

Karyakin, A. & Salem, K. DimmStore: memory power optimization for database systems. Proc. VLDB Endow. 12 , 1499–1512 (2019).

Caset, F., Boussauw, K. & Storme, T. Meet & fly: sustainable transport academics and the elephant in the room. J. Transp. Geogr. 70 , 64–67 (2018).

Govaart, G. H., Hofmann, S. M. & Medawar, E. The sustainability argument for open science. Collabra Psychol. 8 , 35903 (2022).

Cockrell, H. C. et al. Environmental impact of telehealth use for pediatric surgery. J. Pediatr. Surg. 57 , 865–869 (2022).

Alshqaqeeq, F., McGuire, C., Overcash, M., Ali, K. & Twomey, J. Choosing radiology imaging modalities to meet patient needs with lower environmental impact. Resour. Conserv. Recycl. 155 , 104657 (2020).

Sustainability Annual Report 2020–2021 (NHS, 2021); https://digital.nhs.uk/about-nhs-digital/corporate-information-and-documents/sustainability/sustainability-reports/sustainability-annual-report-2020-21

UNESCO Recommendation on Open Science (UNESCO, 2021); https://en.unesco.org/science-sustainable-future/open-science/recommendation

Samuel, G. & Richie, C. Reimagining research ethics to include environmental sustainability: a principled approach, including a case study of data-driven health research. J. Med. Ethics https://doi.org/10.1136/jme-2022-108489 (2022).

Download references

Acknowledgements

L.L. was supported by the University of Cambridge MRC DTP (MR/S502443/1) and the BHF program grant (RG/18/13/33946). M.I. was supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). M.I. was also supported by the UK Economic and Social Research 878 Council (ES/T013192/1). This work was supported by core funding from the British Heart Foundation (RG/13/13/30194; RG/18/13/33946) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland) and the British Heart Foundation and Wellcome.

Author information

Authors and affiliations.

Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

Loïc Lannelongue & Michael Inouye

British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK

Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK

Health Data Research (HDR) UK, London, UK

Hans-Erik G. Aronson, Andrew D. Morris & Gerry Reilly

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK

Alex Bateman, Ewan Birney & Johanna McEntyre

Wellcome Trust, London, UK

Talia Caplan

RAL Space, Science and Technology Facilities Council, Harwell Campus, Didcot, UK

Martin Juckes

Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia

Michael Inouye

British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK

The Alan Turing Institute, London, UK

You can also search for this author in PubMed   Google Scholar

Contributions

L.L. conceived and coordinated the manuscript. M.I. organized and edited the manuscript. All authors contributed to the writing and revision of the manuscript.

Corresponding author

Correspondence to Loïc Lannelongue .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Computational Science thanks Bernabe Dorronsoro and Kirk Cameron for their contribution to the peer review of this work. Primary Handling Editors: Kaitlin McCardle and Ananya Rastogi, in collaboration with the Nature Computational Science team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Lannelongue, L., Aronson, HE.G., Bateman, A. et al. GREENER principles for environmentally sustainable computational science. Nat Comput Sci 3 , 514–521 (2023). https://doi.org/10.1038/s43588-023-00461-y

Download citation

Received : 06 November 2022

Accepted : 09 May 2023

Published : 26 June 2023

Issue Date : June 2023

DOI : https://doi.org/10.1038/s43588-023-00461-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Prioritize environmental sustainability in use of ai and data science methods.

• Caroline Jay
• David Topping

Nature Geoscience (2024)

A holistic approach to environmentally sustainable computing

• Andrea Pazienza
• Giovanni Baselli
• Maria Vittoria Trussoni

Innovations in Systems and Software Engineering (2024)

The carbon footprint of computational research

Nature Computational Science (2023)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Reference management. Clean and simple.

The top list of computer science research databases

1. ACM Digital Library

2. ieee xplore digital library, 3. dblp computer science bibliography, 4. springer lecture notes in computer science (lncs), frequently asked questions about computer science research databases, related articles.

Besides the interdisciplinary research databases Web of Science and Scopus there are also academic databases specifically dedicated to computer science. We have compiled a list of the top 4 research databases with a special focus on computer science to help you find research papers, scholarly articles, and conference papers fast.

ACM Digital Library is the clear number one when it comes to academic databases for computer science. The ACM Full-Text Collection currently has 540,000+ articles, while the ACM Guide to Computing Literature holds more than 2.8+ million bibliographic entries.

• Coverage: 2.8+ million articles
• Abstracts: ✔
• Related articles: ✘
• References: ✔
• Cited by: ✔
• Full text: ✔ (requires institutional subscription)
• Export formats: BibTeX, EndNote

Pro tip: Use a reference manager like Paperpile to keep track of all your sources. Paperpile integrates with ACM Digital Library and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons and later cite them in thousands of citation styles:

IEEE Xplore holds more than 4.7 million research articles from the fields of electrical engineering, computer science, and electronics. It not only covers articles published in scholarly journals, but also conference papers, technical standards, as well as some books.

• Coverage: 4.7+ million articles
• Export formats: BibTeX, RIS

Hosted at the University of Trier, Germany, dbpl has become an indispensable resource in the field of computer science. Its index covers journal articles, conference and workshop proceedings, as well as monographs.

• Coverage: 4.3 million articles
• Abstracts: ✘
• References: ✘
• Cited by: ✘
• Full text: ✘ (Links to publisher websites available)
• Export formats: RIS, BibTeX

Springer's Lecture Notes in Computer Science is the number one publishing source for conference proceedings covering all areas of computer science.

• Coverage: 415,000+ articles
• Export formats: RIS, EndNote, BibTeX

Hosted at the University of Trier, Germany, dbpl has become an indispensable resource in the field of computer science. It's index covers journal articles, conference and workshop proceedings, as well as monographs.

Microsoft Academic was a free academic search engine developed by Microsoft Research. It had more than 13.9 million articles indexed. It was shut down in 2022.

EEE Xplore holds more than 4.7 million research articles from the fields of electrical engineering, computer science, and electronics. It not only covers articles published in scholarly journals, but also conference papers, technical standards, as well as some books.

Journal of Computer Science and Technology

• Sponsored by the Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), and China Computer Federation (CCF).
• Jointly published by ICT, CAS and Springer on a bimonthly basis in English.
• Coverage includes a wide range of topics from computer architecture to artificial intelligence and pattern recognition.
• Promotes insight and understanding of the state of the art, and trends in technology.
• Publishes original research, innovative applications, and surveys/reviews from around the world.

Latest issue

Volume 39, Issue 1

February 3, 2024

Latest articles

Label-aware chinese event detection with heterogeneous graph attention network.

• Shi-Yao Cui
• Jin-Qiao Shi

Minimal Context-Switching Data Race Detection with Dataflow Tracking

Smec: scene mining for e-commerce, identity-preserving adversarial training for robust network embedding.

• Ke-Ting Cen
• Hua-Wei Shen
• Xue-Qi Cheng

4D-MAP: Multipath Adaptive Packet Scheduling for Live Streaming over QUIC

• Cong-Xi Song

Journal updates

Acknowledgement to reviewers.

We do appreciate all the efforts and contributions of the reviewers done for Journal of Computer Science and Technology (JCST). Their professional comments make a huge improvement for the JCST papers. We want to take this opportunity to give our great thanks to them.

Journal information

• ACM Digital Library
• Chinese Science Citation Database
• EI Compendex
• Google Scholar
• OCLC WorldCat Discovery Service
• Science Citation Index Expanded (SCIE)
• TD Net Discovery Service
• UGC-CARE List (India)

Rights and permissions

Springer policies

© Institute of Computing Technology, Chinese Academy of Sciences

• Find a journal
• Publish with us
• Track your research

• Who’s Teaching What
• Subject Updates
• MEng program
• Opportunities
• Minor in Computer Science
• Resources for Current Students
• Program objectives and accreditation
• Graduate program requirements
• Admission process
• Degree programs
• Graduate research
• EECS Graduate Funding
• Resources for current students
• Student profiles
• Instructors
• DEI data and documents
• Recruitment and outreach
• Community and resources
• Get involved / self-education
• Rising Stars in EECS
• Graduate Application Assistance Program (GAAP)
• MIT Summer Research Program (MSRP)
• Sloan-MIT University Center for Exemplary Mentoring (UCEM)
• Electrical Engineering
• Computer Science
• Artificial Intelligence + Decision-making
• AI and Society

AI for Healthcare and Life Sciences

Artificial intelligence and machine learning.

• Biological and Medical Devices and Systems

Communications Systems

• Computational Biology

Computational Fabrication and Manufacturing

Computer architecture, educational technology.

• Electronic, Magnetic, Optical and Quantum Materials and Devices

Graphics and Vision

Human-computer interaction.

• Information Science and Systems
• Integrated Circuits and Systems
• Nanoscale Materials, Devices, and Systems
• Natural Language and Speech Processing
• Optics + Photonics
• Optimization and Game Theory

Programming Languages and Software Engineering

Quantum computing, communication, and sensing, security and cryptography.

• Signal Processing

Systems and Networking

• Systems Theory, Control, and Autonomy

Theory of Computation

• Departmental History
• Departmental Organization
• Visiting Committee
• Explore all research areas

Computer science deals with the theory and practice of algorithms, from idealized mathematical procedures to the computer systems deployed by major tech companies to answer billions of user requests per day.

Primary subareas of this field include: theory, which uses rigorous math to test algorithms’ applicability to certain problems; systems, which develops the underlying hardware and software upon which applications can be implemented; and human-computer interaction, which studies how to make computer systems more effectively meet the needs of real people. The products of all three subareas are applied across science, engineering, medicine, and the social sciences. Computer science drives interdisciplinary collaboration both across MIT and beyond, helping users address the critical societal problems of our era, including opportunity access, climate change, disease, inequality and polarization.

Research areas

Our goal is to develop AI technologies that will change the landscape of healthcare. This includes early diagnostics, drug discovery, care personalization and management. Building on MIT’s pioneering history in artificial intelligence and life sciences, we are working on algorithms suitable for modeling biological and clinical data across a range of modalities including imaging, text and genomics.

Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, …), statistical learning (inference, graphical models, causal analysis, …), deep learning, reinforcement learning, symbolic reasoning ML systems, as well as diverse hardware implementations of ML.

We develop the next generation of wired and wireless communications systems, from new physical principles (e.g., light, terahertz waves) to coding and information theory, and everything in between.

We bring some of the most powerful tools in computation to bear on design problems, including modeling, simulation, processing and fabrication.

We design the next generation of computer systems. Working at the intersection of hardware and software, our research studies how to best implement computation in the physical world. We design processors that are faster, more efficient, easier to program, and secure. Our research covers systems of all scales, from tiny Internet-of-Things devices with ultra-low-power consumption to high-performance servers and datacenters that power planet-scale online services. We design both general-purpose processors and accelerators that are specialized to particular application domains, like machine learning and storage. We also design Electronic Design Automation (EDA) tools to facilitate the development of such systems.

Educational technology combines both hardware and software to enact global change, making education accessible in unprecedented ways to new audiences. We develop the technology that makes better understanding possible.

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

The focus of our research in Human-Computer Interaction (HCI) is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

We develop new approaches to programming, whether that takes the form of programming languages, tools, or methodologies to improve many aspects of applications and systems infrastructure.

Our work focuses on developing the next substrate of computing, communication and sensing. We work all the way from new materials to superconducting devices to quantum computers to theory.

Our research focuses on robotic hardware and algorithms, from sensing to control to perception to manipulation.

Our research is focused on making future computer systems more secure. We bring together a broad spectrum of cross-cutting techniques for security, from theoretical cryptography and programming-language ideas, to low-level hardware and operating-systems security, to overall system designs and empirical bug-finding. We apply these techniques to a wide range of application domains, such as blockchains, cloud systems, Internet privacy, machine learning, and IoT devices, reflecting the growing importance of security in many contexts.

From distributed systems and databases to wireless, the research conducted by the systems and networking group aims to improve the performance, robustness, and ease of management of networks and computing systems.

Theory of Computation (TOC) studies the fundamental strengths and limits of computation, how these strengths and limits interact with computer science and mathematics, and how they manifest themselves in society, biology, and the physical world.

Latest news

Women in stem — a celebration of excellence and curiosity.

An MIT Values event showcased three women’s career journeys and how they are paving the way for the next generation.

A blueprint for making quantum computers easier to program

A CSAIL study highlights why it is so challenging to program a quantum computer to run a quantum algorithm, and offers a conceptual model for a more user-friendly quantum computer.

For more open and equitable public discussions on social media, try “meronymity”

A communication system whose users reveal only a few verified aspects of their identity can empower less confident participants to speak up, researchers report.

Four MIT faculty named 2023 AAAS Fellows

Engelward, Oliver, Rothman, and Vuletić are recognized for their efforts to advance science.

To build a better AI helper, start by modeling the irrational behavior of humans

A new technique can be used to predict the actions of human or AI agents who behave suboptimally while working toward unknown goals.

Upcoming events

Doctoral thesis: data-efficient machine learning for computational imaging, doctoral thesis: graphs of convex sets with applications to optimal control and motion planning, doctoral thesis: learning the language of bimolecular interactions, doctoral thesis: automated interpretation of machine learning models, doctoral thesis: motion-robust machine learning methods for region-of-interest tracking and selective magnetic resonance imaging with external shim arrays, doctoral thesis: programming machines and materials for on-demand assembly.

Journal of Computer Science

Aims and scope.

The Journal of Computer Science (JCS) is dedicated to advancing computer science by publishing high-quality research and review articles that span both theoretical foundations and practical applications in information, computation, and computer systems. With a commitment to excellence, JCS offers a platform for researchers, scholars, and industry professionals to share their insights and contribute to the ongoing evolution of computer science. Published on a monthly basis, JCS provides up-to-date insights into this ever-evolving discipline.

Science Publications is pleased to announce the launch of a new open access journal, Journal of Adaptive Structures. JAS brings together emerging technologies for adaptive smart structures, including advanced materials, smart actuation, sensing and control, to pursue the progressive adoption of the major scientific achievements in this multidisciplinary field on-board of commercial aircraft.

It is with great pleasure that we announce the SGAMR Annual Awards 2020. This award is given annually to Researchers and Reviewers of International Journal of Structural Glass and Advanced Materials Research (SGAMR) who have shown innovative contributions and promising research as well as others who have excelled in their Editorial duties.

This special issue "Neuroinflammation and COVID-19" aims to provide a space for debate in the face of the growing evidence on the affectation of the nervous system by COVID-19, supported by original studies and case series.

• Recently Published
• Most Viewed
• Most Downloaded

• Privacy Policy

Buy Me a Coffee

Home » 500+ Computer Science Research Topics

500+ Computer Science Research Topics

Computer Science is a constantly evolving field that has transformed the world we live in today. With new technologies emerging every day, there are countless research opportunities in this field. Whether you are interested in artificial intelligence, machine learning, cybersecurity, data analytics, or computer networks, there are endless possibilities to explore. In this post, we will delve into some of the most interesting and important research topics in Computer Science. From the latest advancements in programming languages to the development of cutting-edge algorithms, we will explore the latest trends and innovations that are shaping the future of Computer Science. So, whether you are a student or a professional, read on to discover some of the most exciting research topics in this dynamic and rapidly expanding field.

Computer Science Research Topics

Computer Science Research Topics are as follows:

• Using machine learning to detect and prevent cyber attacks
• Developing algorithms for optimized resource allocation in cloud computing
• Investigating the use of blockchain technology for secure and decentralized data storage
• Developing intelligent chatbots for customer service
• Investigating the effectiveness of deep learning for natural language processing
• Developing algorithms for detecting and removing fake news from social media
• Investigating the impact of social media on mental health
• Developing algorithms for efficient image and video compression
• Investigating the use of big data analytics for predictive maintenance in manufacturing
• Developing algorithms for identifying and mitigating bias in machine learning models
• Investigating the ethical implications of autonomous vehicles
• Developing algorithms for detecting and preventing cyberbullying
• Investigating the use of machine learning for personalized medicine
• Developing algorithms for efficient and accurate speech recognition
• Investigating the impact of social media on political polarization
• Developing algorithms for sentiment analysis in social media data
• Investigating the use of virtual reality in education
• Developing algorithms for efficient data encryption and decryption
• Investigating the impact of technology on workplace productivity
• Developing algorithms for detecting and mitigating deepfakes
• Investigating the use of artificial intelligence in financial trading
• Developing algorithms for efficient database management
• Investigating the effectiveness of online learning platforms
• Developing algorithms for efficient and accurate facial recognition
• Investigating the use of machine learning for predicting weather patterns
• Developing algorithms for efficient and secure data transfer
• Investigating the impact of technology on social skills and communication
• Developing algorithms for efficient and accurate object recognition
• Investigating the use of machine learning for fraud detection in finance
• Developing algorithms for efficient and secure authentication systems
• Investigating the impact of technology on privacy and surveillance
• Developing algorithms for efficient and accurate handwriting recognition
• Investigating the use of machine learning for predicting stock prices
• Developing algorithms for efficient and secure biometric identification
• Investigating the impact of technology on mental health and well-being
• Developing algorithms for efficient and accurate language translation
• Investigating the use of machine learning for personalized advertising
• Developing algorithms for efficient and secure payment systems
• Investigating the impact of technology on the job market and automation
• Developing algorithms for efficient and accurate object tracking
• Investigating the use of machine learning for predicting disease outbreaks
• Developing algorithms for efficient and secure access control
• Investigating the impact of technology on human behavior and decision making
• Developing algorithms for efficient and accurate sound recognition
• Investigating the use of machine learning for predicting customer behavior
• Developing algorithms for efficient and secure data backup and recovery
• Investigating the impact of technology on education and learning outcomes
• Developing algorithms for efficient and accurate emotion recognition
• Investigating the use of machine learning for improving healthcare outcomes
• Developing algorithms for efficient and secure supply chain management
• Investigating the impact of technology on cultural and societal norms
• Developing algorithms for efficient and accurate gesture recognition
• Investigating the use of machine learning for predicting consumer demand
• Developing algorithms for efficient and secure cloud storage
• Investigating the impact of technology on environmental sustainability
• Developing algorithms for efficient and accurate voice recognition
• Investigating the use of machine learning for improving transportation systems
• Developing algorithms for efficient and secure mobile device management
• Investigating the impact of technology on social inequality and access to resources
• Machine learning for healthcare diagnosis and treatment
• Machine Learning for Cybersecurity
• Machine learning for personalized medicine
• Cybersecurity threats and defense strategies
• Big data analytics for business intelligence
• Blockchain technology and its applications
• Human-computer interaction in virtual reality environments
• Artificial intelligence for autonomous vehicles
• Natural language processing for chatbots
• Cloud computing and its impact on the IT industry
• Internet of Things (IoT) and smart homes
• Robotics and automation in manufacturing
• Augmented reality and its potential in education
• Data mining techniques for customer relationship management
• Computer vision for object recognition and tracking
• Quantum computing and its applications in cryptography
• Social media analytics and sentiment analysis
• Recommender systems for personalized content delivery
• Mobile computing and its impact on society
• Bioinformatics and genomic data analysis
• Deep learning for image and speech recognition
• Digital signal processing and audio processing algorithms
• Cloud storage and data security in the cloud
• Wearable technology and its impact on healthcare
• Computational linguistics for natural language understanding
• Cognitive computing for decision support systems
• Cyber-physical systems and their applications
• Edge computing and its impact on IoT
• Machine learning for fraud detection
• Cryptography and its role in secure communication
• Cybersecurity risks in the era of the Internet of Things
• Natural language generation for automated report writing
• 3D printing and its impact on manufacturing
• Virtual assistants and their applications in daily life
• Cloud-based gaming and its impact on the gaming industry
• Computer networks and their security issues
• Cyber forensics and its role in criminal investigations
• Machine learning for predictive maintenance in industrial settings
• Augmented reality for cultural heritage preservation
• Human-robot interaction and its applications
• Data visualization and its impact on decision-making
• Cybersecurity in financial systems and blockchain
• Computer graphics and animation techniques
• Biometrics and its role in secure authentication
• Cloud-based e-learning platforms and their impact on education
• Natural language processing for machine translation
• Machine learning for predictive maintenance in healthcare
• Cybersecurity and privacy issues in social media
• Computer vision for medical image analysis
• Natural language generation for content creation
• Cybersecurity challenges in cloud computing
• Human-robot collaboration in manufacturing
• Data mining for predicting customer churn
• Artificial intelligence for autonomous drones
• Cybersecurity risks in the healthcare industry
• Machine learning for speech synthesis
• Edge computing for low-latency applications
• Virtual reality for mental health therapy
• Quantum computing and its applications in finance
• Biomedical engineering and its applications
• Cybersecurity in autonomous systems
• Machine learning for predictive maintenance in transportation
• Computer vision for object detection in autonomous driving
• Augmented reality for industrial training and simulations
• Cloud-based cybersecurity solutions for small businesses
• Natural language processing for knowledge management
• Machine learning for personalized advertising
• Cybersecurity in the supply chain management
• Cybersecurity risks in the energy sector
• Computer vision for facial recognition
• Natural language processing for social media analysis
• Machine learning for sentiment analysis in customer reviews
• Explainable Artificial Intelligence
• Quantum Computing
• Blockchain Technology
• Human-Computer Interaction
• Natural Language Processing
• Cloud Computing
• Robotics and Automation
• Augmented Reality and Virtual Reality
• Cyber-Physical Systems
• Computational Neuroscience
• Big Data Analytics
• Computer Vision
• Cryptography and Network Security
• Internet of Things
• Computer Graphics and Visualization
• Artificial Intelligence for Game Design
• Computational Biology
• Social Network Analysis
• Bioinformatics
• Distributed Systems and Middleware
• Information Retrieval and Data Mining
• Computer Networks
• Mobile Computing and Wireless Networks
• Software Engineering
• Database Systems
• Parallel and Distributed Computing
• Human-Robot Interaction
• Intelligent Transportation Systems
• High-Performance Computing
• Cyber-Physical Security
• Deep Learning
• Sensor Networks
• Multi-Agent Systems
• Human-Centered Computing
• Wearable Computing
• Knowledge Representation and Reasoning
• Adaptive Systems
• Brain-Computer Interface
• Health Informatics
• Cognitive Computing
• Cybersecurity and Privacy
• Internet Security
• Cybercrime and Digital Forensics
• Cloud Security
• Cryptocurrencies and Digital Payments
• Machine Learning for Natural Language Generation
• Cognitive Robotics
• Neural Networks
• Semantic Web
• Image Processing
• Cyber Threat Intelligence
• Secure Mobile Computing
• Cybersecurity Education and Training
• Privacy Preserving Techniques
• Cyber-Physical Systems Security
• Virtualization and Containerization
• Machine Learning for Computer Vision
• Network Function Virtualization
• Cybersecurity Risk Management
• Information Security Governance
• Intrusion Detection and Prevention
• Biometric Authentication
• Machine Learning for Predictive Maintenance
• Security in Cloud-based Environments
• Cybersecurity for Industrial Control Systems
• Smart Grid Security
• Software Defined Networking
• Quantum Cryptography
• Security in the Internet of Things
• Natural language processing for sentiment analysis
• Blockchain technology for secure data sharing
• Developing efficient algorithms for big data analysis
• Cybersecurity for internet of things (IoT) devices
• Human-robot interaction for industrial automation
• Image recognition for autonomous vehicles
• Social media analytics for marketing strategy
• Quantum computing for solving complex problems
• Biometric authentication for secure access control
• Augmented reality for education and training
• Intelligent transportation systems for traffic management
• Predictive modeling for financial markets
• Cloud computing for scalable data storage and processing
• Virtual reality for therapy and mental health treatment
• Data visualization for business intelligence
• Recommender systems for personalized product recommendations
• Speech recognition for voice-controlled devices
• Mobile computing for real-time location-based services
• Neural networks for predicting user behavior
• Genetic algorithms for optimization problems
• Distributed computing for parallel processing
• Internet of things (IoT) for smart cities
• Wireless sensor networks for environmental monitoring
• Cloud-based gaming for high-performance gaming
• Social network analysis for identifying influencers
• Autonomous systems for agriculture
• Robotics for disaster response
• Data mining for customer segmentation
• Computer graphics for visual effects in movies and video games
• Virtual assistants for personalized customer service
• Natural language understanding for chatbots
• 3D printing for manufacturing prototypes
• Artificial intelligence for stock trading
• Machine learning for weather forecasting
• Biomedical engineering for prosthetics and implants
• Cybersecurity for financial institutions
• Machine learning for energy consumption optimization
• Computer vision for object tracking
• Natural language processing for document summarization
• Wearable technology for health and fitness monitoring
• Internet of things (IoT) for home automation
• Reinforcement learning for robotics control
• Big data analytics for customer insights
• Machine learning for supply chain optimization
• Natural language processing for legal document analysis
• Artificial intelligence for drug discovery
• Computer vision for object recognition in robotics
• Data mining for customer churn prediction
• Autonomous systems for space exploration
• Robotics for agriculture automation
• Machine learning for predicting earthquakes
• Natural language processing for sentiment analysis in customer reviews
• Big data analytics for predicting natural disasters
• Internet of things (IoT) for remote patient monitoring
• Blockchain technology for digital identity management
• Machine learning for predicting wildfire spread
• Computer vision for gesture recognition
• Natural language processing for automated translation
• Big data analytics for fraud detection in banking
• Internet of things (IoT) for smart homes
• Robotics for warehouse automation
• Machine learning for predicting air pollution
• Natural language processing for medical record analysis
• Augmented reality for architectural design
• Big data analytics for predicting traffic congestion
• Machine learning for predicting customer lifetime value
• Developing algorithms for efficient and accurate text recognition
• Natural Language Processing for Virtual Assistants
• Natural Language Processing for Sentiment Analysis in Social Media
• Explainable Artificial Intelligence (XAI) for Trust and Transparency
• Deep Learning for Image and Video Retrieval
• Edge Computing for Internet of Things (IoT) Applications
• Data Science for Social Media Analytics
• Cybersecurity for Critical Infrastructure Protection
• Natural Language Processing for Text Classification
• Quantum Computing for Optimization Problems
• Machine Learning for Personalized Health Monitoring
• Computer Vision for Autonomous Driving
• Blockchain Technology for Supply Chain Management
• Augmented Reality for Education and Training
• Natural Language Processing for Sentiment Analysis
• Machine Learning for Personalized Marketing
• Big Data Analytics for Financial Fraud Detection
• Cybersecurity for Cloud Security Assessment
• Artificial Intelligence for Natural Language Understanding
• Blockchain Technology for Decentralized Applications
• Virtual Reality for Cultural Heritage Preservation
• Natural Language Processing for Named Entity Recognition
• Machine Learning for Customer Churn Prediction
• Big Data Analytics for Social Network Analysis
• Cybersecurity for Intrusion Detection and Prevention
• Artificial Intelligence for Robotics and Automation
• Blockchain Technology for Digital Identity Management
• Virtual Reality for Rehabilitation and Therapy
• Natural Language Processing for Text Summarization
• Machine Learning for Credit Risk Assessment
• Big Data Analytics for Fraud Detection in Healthcare
• Cybersecurity for Internet Privacy Protection
• Artificial Intelligence for Game Design and Development
• Blockchain Technology for Decentralized Social Networks
• Virtual Reality for Marketing and Advertising
• Natural Language Processing for Opinion Mining
• Machine Learning for Anomaly Detection
• Big Data Analytics for Predictive Maintenance in Transportation
• Cybersecurity for Network Security Management
• Artificial Intelligence for Personalized News and Content Delivery
• Blockchain Technology for Cryptocurrency Mining
• Virtual Reality for Architectural Design and Visualization
• Natural Language Processing for Machine Translation
• Machine Learning for Automated Image Captioning
• Big Data Analytics for Stock Market Prediction
• Cybersecurity for Biometric Authentication Systems
• Artificial Intelligence for Human-Robot Interaction
• Blockchain Technology for Smart Grids
• Virtual Reality for Sports Training and Simulation
• Natural Language Processing for Question Answering Systems
• Machine Learning for Sentiment Analysis in Customer Feedback
• Big Data Analytics for Predictive Maintenance in Manufacturing
• Cybersecurity for Cloud-Based Systems
• Artificial Intelligence for Automated Journalism
• Blockchain Technology for Intellectual Property Management
• Virtual Reality for Therapy and Rehabilitation
• Natural Language Processing for Language Generation
• Machine Learning for Customer Lifetime Value Prediction
• Big Data Analytics for Predictive Maintenance in Energy Systems
• Cybersecurity for Secure Mobile Communication
• Artificial Intelligence for Emotion Recognition
• Blockchain Technology for Digital Asset Trading
• Virtual Reality for Automotive Design and Visualization
• Natural Language Processing for Semantic Web
• Machine Learning for Fraud Detection in Financial Transactions
• Big Data Analytics for Social Media Monitoring
• Cybersecurity for Cloud Storage and Sharing
• Artificial Intelligence for Personalized Education
• Blockchain Technology for Secure Online Voting Systems
• Virtual Reality for Cultural Tourism
• Natural Language Processing for Chatbot Communication
• Machine Learning for Medical Diagnosis and Treatment
• Big Data Analytics for Environmental Monitoring and Management.
• Cybersecurity for Cloud Computing Environments
• Virtual Reality for Training and Simulation
• Big Data Analytics for Sports Performance Analysis
• Cybersecurity for Internet of Things (IoT) Devices
• Artificial Intelligence for Traffic Management and Control
• Blockchain Technology for Smart Contracts
• Natural Language Processing for Document Summarization
• Machine Learning for Image and Video Recognition
• Blockchain Technology for Digital Asset Management
• Virtual Reality for Entertainment and Gaming
• Natural Language Processing for Opinion Mining in Online Reviews
• Machine Learning for Customer Relationship Management
• Big Data Analytics for Environmental Monitoring and Management
• Cybersecurity for Network Traffic Analysis and Monitoring
• Artificial Intelligence for Natural Language Generation
• Blockchain Technology for Supply Chain Transparency and Traceability
• Virtual Reality for Design and Visualization
• Natural Language Processing for Speech Recognition
• Machine Learning for Recommendation Systems
• Big Data Analytics for Customer Segmentation and Targeting
• Cybersecurity for Biometric Authentication
• Artificial Intelligence for Human-Computer Interaction
• Blockchain Technology for Decentralized Finance (DeFi)
• Virtual Reality for Tourism and Cultural Heritage
• Machine Learning for Cybersecurity Threat Detection and Prevention
• Big Data Analytics for Healthcare Cost Reduction
• Cybersecurity for Data Privacy and Protection
• Artificial Intelligence for Autonomous Vehicles
• Blockchain Technology for Cryptocurrency and Blockchain Security
• Virtual Reality for Real Estate Visualization
• Natural Language Processing for Question Answering
• Big Data Analytics for Financial Markets Prediction
• Cybersecurity for Cloud-Based Machine Learning Systems
• Artificial Intelligence for Personalized Advertising
• Blockchain Technology for Digital Identity Verification
• Virtual Reality for Cultural and Language Learning
• Natural Language Processing for Semantic Analysis
• Machine Learning for Business Forecasting
• Big Data Analytics for Social Media Marketing
• Artificial Intelligence for Content Generation
• Blockchain Technology for Smart Cities
• Virtual Reality for Historical Reconstruction
• Natural Language Processing for Knowledge Graph Construction
• Machine Learning for Speech Synthesis
• Big Data Analytics for Traffic Optimization
• Artificial Intelligence for Social Robotics
• Blockchain Technology for Healthcare Data Management
• Virtual Reality for Disaster Preparedness and Response
• Natural Language Processing for Multilingual Communication
• Machine Learning for Emotion Recognition
• Big Data Analytics for Human Resources Management
• Cybersecurity for Mobile App Security
• Artificial Intelligence for Financial Planning and Investment
• Blockchain Technology for Energy Management
• Virtual Reality for Cultural Preservation and Heritage.
• Big Data Analytics for Healthcare Management
• Cybersecurity in the Internet of Things (IoT)
• Artificial Intelligence for Predictive Maintenance
• Computational Biology for Drug Discovery
• Virtual Reality for Mental Health Treatment
• Machine Learning for Sentiment Analysis in Social Media
• Human-Computer Interaction for User Experience Design
• Cloud Computing for Disaster Recovery
• Quantum Computing for Cryptography
• Intelligent Transportation Systems for Smart Cities
• Cybersecurity for Autonomous Vehicles
• Artificial Intelligence for Fraud Detection in Financial Systems
• Social Network Analysis for Marketing Campaigns
• Cloud Computing for Video Game Streaming
• Machine Learning for Speech Recognition
• Augmented Reality for Architecture and Design
• Natural Language Processing for Customer Service Chatbots
• Machine Learning for Climate Change Prediction
• Big Data Analytics for Social Sciences
• Artificial Intelligence for Energy Management
• Virtual Reality for Tourism and Travel
• Cybersecurity for Smart Grids
• Machine Learning for Image Recognition
• Augmented Reality for Sports Training
• Natural Language Processing for Content Creation
• Cloud Computing for High-Performance Computing
• Artificial Intelligence for Personalized Medicine
• Virtual Reality for Architecture and Design
• Augmented Reality for Product Visualization
• Natural Language Processing for Language Translation
• Cybersecurity for Cloud Computing
• Artificial Intelligence for Supply Chain Optimization
• Blockchain Technology for Digital Voting Systems
• Virtual Reality for Job Training
• Augmented Reality for Retail Shopping
• Natural Language Processing for Sentiment Analysis in Customer Feedback
• Cloud Computing for Mobile Application Development
• Artificial Intelligence for Cybersecurity Threat Detection
• Blockchain Technology for Intellectual Property Protection
• Virtual Reality for Music Education
• Machine Learning for Financial Forecasting
• Augmented Reality for Medical Education
• Natural Language Processing for News Summarization
• Cybersecurity for Healthcare Data Protection
• Artificial Intelligence for Autonomous Robots
• Virtual Reality for Fitness and Health
• Machine Learning for Natural Language Understanding
• Augmented Reality for Museum Exhibits
• Natural Language Processing for Chatbot Personality Development
• Cloud Computing for Website Performance Optimization
• Artificial Intelligence for E-commerce Recommendation Systems
• Blockchain Technology for Supply Chain Traceability
• Virtual Reality for Military Training
• Augmented Reality for Advertising
• Natural Language Processing for Chatbot Conversation Management
• Cybersecurity for Cloud-Based Services
• Artificial Intelligence for Agricultural Management
• Blockchain Technology for Food Safety Assurance
• Virtual Reality for Historical Reenactments
• Machine Learning for Cybersecurity Incident Response.
• Secure Multiparty Computation
• Federated Learning
• Internet of Things Security
• Blockchain Scalability
• Quantum Computing Algorithms
• Explainable AI
• Data Privacy in the Age of Big Data
• Adversarial Machine Learning
• Deep Reinforcement Learning
• Online Learning and Streaming Algorithms
• Graph Neural Networks
• Automated Debugging and Fault Localization
• Mobile Application Development
• Software Engineering for Cloud Computing
• Cryptocurrency Security
• Edge Computing for Real-Time Applications
• Natural Language Generation
• Virtual and Augmented Reality
• Computational Biology and Bioinformatics
• Internet of Things Applications
• Robotics and Autonomous Systems
• Explainable Robotics
• 3D Printing and Additive Manufacturing
• Distributed Systems
• Parallel Computing
• Data Center Networking
• Data Mining and Knowledge Discovery
• Information Retrieval and Search Engines
• Network Security and Privacy
• Cloud Computing Security
• Data Analytics for Business Intelligence
• Neural Networks and Deep Learning
• Reinforcement Learning for Robotics
• Automated Planning and Scheduling
• Evolutionary Computation and Genetic Algorithms
• Formal Methods for Software Engineering
• Computational Complexity Theory
• Bio-inspired Computing
• Computer Vision for Object Recognition
• Automated Reasoning and Theorem Proving
• Natural Language Understanding
• Machine Learning for Healthcare
• Scalable Distributed Systems
• Sensor Networks and Internet of Things
• Smart Grids and Energy Systems
• Software Testing and Verification
• Web Application Security
• Wireless and Mobile Networks
• Computer Architecture and Hardware Design
• Digital Signal Processing
• Game Theory and Mechanism Design
• Multi-agent Systems
• Evolutionary Robotics
• Quantum Machine Learning
• Computational Social Science
• Explainable Recommender Systems.
• Artificial Intelligence and its applications
• Cloud computing and its benefits
• Cybersecurity threats and solutions
• Internet of Things and its impact on society
• Virtual and Augmented Reality and its uses
• Blockchain Technology and its potential in various industries
• Web Development and Design
• Digital Marketing and its effectiveness
• Big Data and Analytics
• Software Development Life Cycle
• Gaming Development and its growth
• Network Administration and Maintenance
• Machine Learning and its uses
• Data Warehousing and Mining
• Computer Architecture and Design
• Computer Graphics and Animation
• Quantum Computing and its potential
• Data Structures and Algorithms
• Computer Vision and Image Processing
• Robotics and its applications
• Operating Systems and its functions
• Information Theory and Coding
• Compiler Design and Optimization
• Computer Forensics and Cyber Crime Investigation
• Distributed Computing and its significance
• Artificial Neural Networks and Deep Learning
• Cloud Storage and Backup
• Programming Languages and their significance
• Computer Simulation and Modeling
• Computer Networks and its types
• Information Security and its types
• Computer-based Training and eLearning
• Medical Imaging and its uses
• Social Media Analysis and its applications
• Human Resource Information Systems
• Computer-Aided Design and Manufacturing
• Multimedia Systems and Applications
• Geographic Information Systems and its uses
• Computer-Assisted Language Learning
• Mobile Device Management and Security
• Data Compression and its types
• Knowledge Management Systems
• Text Mining and its uses
• Cyber Warfare and its consequences
• Wireless Networks and its advantages
• Computer Ethics and its importance
• Computational Linguistics and its applications
• Autonomous Systems and Robotics
• Information Visualization and its importance
• Geographic Information Retrieval and Mapping
• Business Intelligence and its benefits
• Digital Libraries and their significance
• Artificial Life and Evolutionary Computation
• Computer Music and its types
• Virtual Teams and Collaboration
• Computer Games and Learning
• Semantic Web and its applications
• Electronic Commerce and its advantages
• Multimedia Databases and their significance
• Computer Science Education and its importance
• Computer-Assisted Translation and Interpretation
• Ambient Intelligence and Smart Homes
• Autonomous Agents and Multi-Agent Systems.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

200+ Funny Research Topics

500+ Sports Research Topics

300+ American History Research Paper Topics

500+ Cyber Security Research Topics

500+ Environmental Research Topics

500+ Economics Research Topics

• MyU : For Students, Faculty, and Staff

Fall 2024 CSCI Special Topics Courses

Cloud computing.

Meeting Time: 09:45 AM‑11:00 AM TTh  Instructor: Ali Anwar Course Description: Cloud computing serves many large-scale applications ranging from search engines like Google to social networking websites like Facebook to online stores like Amazon. More recently, cloud computing has emerged as an essential technology to enable emerging fields such as Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning. The exponential growth of data availability and demands for security and speed has made the cloud computing paradigm necessary for reliable, financially economical, and scalable computation. The dynamicity and flexibility of Cloud computing have opened up many new forms of deploying applications on infrastructure that cloud service providers offer, such as renting of computation resources and serverless computing.    This course will cover the fundamentals of cloud services management and cloud software development, including but not limited to design patterns, application programming interfaces, and underlying middleware technologies. More specifically, we will cover the topics of cloud computing service models, data centers resource management, task scheduling, resource virtualization, SLAs, cloud security, software defined networks and storage, cloud storage, and programming models. We will also discuss data center design and management strategies, which enable the economic and technological benefits of cloud computing. Lastly, we will study cloud storage concepts like data distribution, durability, consistency, and redundancy. Registration Prerequisites: CS upper div, CompE upper div., EE upper div., EE grad, ITI upper div., Univ. honors student, or dept. permission; no cr for grads in CSci. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/6BvbUwEkBK41tPJ17 ).

CSCI 5980/8980 

Machine learning for healthcare: concepts and applications.

Meeting Time: 11:15 AM‑12:30 PM TTh  Instructor: Yogatheesan Varatharajah Course Description: Machine Learning is transforming healthcare. This course will introduce students to a range of healthcare problems that can be tackled using machine learning, different health data modalities, relevant machine learning paradigms, and the unique challenges presented by healthcare applications. Applications we will cover include risk stratification, disease progression modeling, precision medicine, diagnosis, prognosis, subtype discovery, and improving clinical workflows. We will also cover research topics such as explainability, causality, trust, robustness, and fairness.

Registration Prerequisites: CSCI 5521 or equivalent. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/z8X9pVZfCWMpQQ6o6  ).

Visualization with AI

Meeting Time: 04:00 PM‑05:15 PM TTh  Instructor: Qianwen Wang Course Description: This course aims to investigate how visualization techniques and AI technologies work together to enhance understanding, insights, or outcomes.

This is a seminar style course consisting of lectures, paper presentation, and interactive discussion of the selected papers. Students will also work on a group project where they propose a research idea, survey related studies, and present initial results.

This course will cover the application of visualization to better understand AI models and data, and the use of AI to improve visualization processes. Readings for the course cover papers from the top venues of AI, Visualization, and HCI, topics including AI explainability, reliability, and Human-AI collaboration.    This course is designed for PhD students, Masters students, and advanced undergraduates who want to dig into research.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/YTF5EZFUbQRJhHBYA  ). Although the class is primarily intended for PhD students, motivated juniors/seniors and MS students who are interested in this topic are welcome to apply, ensuring they detail their qualifications for the course.

Visualizations for Intelligent AR Systems

Meeting Time: 04:00 PM‑05:15 PM MW  Instructor: Zhu-Tian Chen Course Description: This course aims to explore the role of Data Visualization as a pivotal interface for enhancing human-data and human-AI interactions within Augmented Reality (AR) systems, thereby transforming a broad spectrum of activities in both professional and daily contexts. Structured as a seminar, the course consists of two main components: the theoretical and conceptual foundations delivered through lectures, paper readings, and discussions; and the hands-on experience gained through small assignments and group projects. This class is designed to be highly interactive, and AR devices will be provided to facilitate hands-on learning.    Participants will have the opportunity to experience AR systems, develop cutting-edge AR interfaces, explore AI integration, and apply human-centric design principles. The course is designed to advance students' technical skills in AR and AI, as well as their understanding of how these technologies can be leveraged to enrich human experiences across various domains. Students will be encouraged to create innovative projects with the potential for submission to research conferences.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/Y81FGaJivoqMQYtq5 ). Students are expected to have a solid foundation in either data visualization, computer graphics, computer vision, or HCI. Having expertise in all would be perfect! However, a robust interest and eagerness to delve into these subjects can be equally valuable, even though it means you need to learn some basic concepts independently.

Sustainable Computing: A Systems View

Meeting Time: 09:45 AM‑11:00 AM  Instructor: Abhishek Chandra Course Description: In recent years, there has been a dramatic increase in the pervasiveness, scale, and distribution of computing infrastructure: ranging from cloud, HPC systems, and data centers to edge computing and pervasive computing in the form of micro-data centers, mobile phones, sensors, and IoT devices embedded in the environment around us. The growing amount of computing, storage, and networking demand leads to increased energy usage, carbon emissions, and natural resource consumption. To reduce their environmental impact, there is a growing need to make computing systems sustainable. In this course, we will examine sustainable computing from a systems perspective. We will examine a number of questions:   • How can we design and build sustainable computing systems?   • How can we manage resources efficiently?   • What system software and algorithms can reduce computational needs?    Topics of interest would include:   • Sustainable system design and architectures   • Sustainability-aware systems software and management   • Sustainability in large-scale distributed computing (clouds, data centers, HPC)   • Sustainability in dispersed computing (edge, mobile computing, sensors/IoT)

Registration Prerequisites: This course is targeted towards students with a strong interest in computer systems (Operating Systems, Distributed Systems, Networking, Databases, etc.). Background in Operating Systems (Equivalent of CSCI 5103) and basic understanding of Computer Networking (Equivalent of CSCI 4211) is required.

• Future undergraduate students
• Future transfer students
• Future graduate students
• Future international students
• Diversity and Inclusion Opportunities
• Learn abroad
• Living Learning Communities
• Mentor programs
• Programs for women
• Student groups
• Visit, Apply & Next Steps
• Information for current students
• Departments and majors overview
• Departments
• Undergraduate majors
• Graduate programs
• Integrated Degree Programs
• Additional degree-granting programs
• Online learning
• Academic Advising overview
• Academic Advising FAQ
• Academic Advising Blog
• Appointments and drop-ins
• Academic support
• Commencement
• Four-year plans
• Honors advising
• Policies, procedures, and forms
• Career Services overview
• Resumes and cover letters
• Jobs and internships
• Interviews and job offers
• CSE Career Fair
• Major and career exploration
• Graduate school
• Collegiate Life overview
• Scholarships
• Diversity & Inclusivity Alliance
• Anderson Student Innovation Labs
• Information for alumni
• Get engaged with CSE
• Upcoming events
• CSE Alumni Society Board
• Alumni volunteer interest form
• Golden Medallion Society Reunion
• 50-Year Reunion
• Alumni honors and awards
• Outstanding Achievement
• Alumni Service
• Distinguished Leadership
• Honorary Doctorate Degrees
• Nobel Laureates
• Alumni resources
• Alumni career resources
• Alumni news outlets
• CSE branded clothing
• International alumni resources
• Inventing Tomorrow magazine
• Update your info
• CSE giving overview
• Why give to CSE?
• College priorities
• Give online now
• External relations
• Giving priorities
• Donor stories
• Impact of giving
• Ways to give to CSE
• Matching gifts
• CSE directories
• Invest in your company and the future
• Recruit our students
• Connect with researchers
• K-12 initiatives
• Diversity initiatives
• Research news
• Give to CSE
• CSE priorities
• Corporate relations
• Information for faculty and staff
• Administrative offices overview
• Office of the Dean
• Academic affairs
• Finance and Operations
• Communications
• Human resources
• Undergraduate programs and student services
• CSE Committees
• CSE policies overview
• Academic policies
• Faculty hiring and tenure policies
• Finance policies and information
• Graduate education policies
• Human resources policies
• Research policies
• Research overview
• Research centers and facilities
• Research proposal submission process
• Research safety
• Award-winning CSE faculty
• National academies
• University awards
• Honorary professorships
• Collegiate awards
• Other CSE honors and awards
• Staff awards
• Performance Management Process
• Work. With Flexibility in CSE
• K-12 outreach overview
• Summer camps
• Outreach events
• Enrichment programs
• Field trips and tours
• CSE K-12 Virtual Classroom Resources
• Educator development
• Sponsor an event

Paper: To understand cognition--and its dysfunction--neuroscientists must learn its rhythms

Thought emerges and is controlled in the brain via the rhythmically and spatially coordinated activity of millions of neurons, scientists argue in a new article. understanding cognition and its disorders requires studying it at that level.

Thought emerges and is controlled in the brain via the rhythmically and spatially coordinated activity of millions of neurons, scientists argue in a new article. Understanding cognition and its disorders requires studying it at that level.

It could be very informative to observe the pixels on your phone under a microscope, but not if your goal is to understand what a whole video on the screen shows. Cognition is much the same kind of emergent property in the brain . It can only be understood by observing how millions of cells act in coordination, argues a trio of MIT neuroscientists. In a new article , they lay out a framework for understanding how thought arises from the coordination of neural activity driven by oscillating electric fields -- also known as brain "waves" or "rhythms."

Historically dismissed solely as byproducts of neural activity, brain rhythms are actually critical for organizing it, write Picower Professor Earl Miller and research scientists Scott Brincat and Jefferson Roy in Current Opinion in Behavioral Science . And while neuroscientists have gained tremendous knowledge from studying how individual brain cells connect and how and when they emit "spikes" to send impulses through specific circuits, there is also a need to appreciate and apply new concepts at the brain rhythm scale, which can span individual, or even multiple, brain regions.

"Spiking and anatomy are important but there is more going on in the brain above and beyond that," said senior author Miller, a faculty member in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT. "There's a whole lot of functionality taking place at a higher level, especially cognition."

The stakes of studying the brain at that scale, the authors write, might not only include understanding healthy higher-level function but also how those functions become disrupted in disease.

"Many neurological and psychiatric disorders, such as schizophrenia, epilepsy and Parkinson's involve disruption of emergent properties like neural synchrony," they write. "We anticipate that understanding how to interpret and interface with these emergent properties will be critical for developing effective treatments as well as understanding cognition."

The emergence of thoughts

The bridge between the scale of individual neurons and the broader-scale coordination of many cells is founded on electric fields, the researchers write. Via a phenomenon called "ephaptic coupling," the electrical field generated by the activity of a neuron can influence the voltage of neighboring neurons, creating an alignment among them. In this way, electric fields both reflect neural activity but also influence it. In a paper in 2022, Miller and colleagues showed via experiments and computational modeling that the information encoded in the electric fields generated by ensembles of neurons can be read out more reliably than the information encoded by the spikes of individual cells. In 2023 Miller's lab provided evidence that rhythmic electrical fields may coordinate memories between regions.

At this larger scale, in which rhythmic electric fields carry information between brain regions, Miller's lab has published numerous studies showing that lower-frequency rhythms in the so-called "beta" band originate in deeper layers of the brain's cortex and appear to regulate the power of faster-frequency "gamma" rhythms in more superficial layers. By recording neural activity in the brains of animals engaged in working memory games the lab has shown that beta rhythms carry "top down" signals to control when and where gamma rhythms can encode sensory information, such as the images that the animals need to remember in the game.

Some of the lab's latest evidence suggests that beta rhythms apply this control of cognitive processes to physical patches of the cortex, essentially acting like stencils that pattern where and when gamma can encode sensory information into memory, or retrieve it. According to this theory, which Miller calls "Spatial Computing," beta can thereby establish the general rules of a task (for instance, the back and forth turns required to open a combination lock), even as the specific information content may change (for instance, new numbers when the combination changes). More generally, this structure also enables neurons to flexibly encode more than one kind of information at a time, the authors write, a widely observed neural property called "mixed selectivity." For instance, a neuron encoding a number of the lock combination can also be assigned, based on which beta-stenciled patch it is in, the particular step of the unlocking process that the number matters for.

In the new study Miller, Brincat and Roy suggest another advantage consistent with cognitive control being based on an interplay of large-scale coordinated rhythmic activity: "Subspace coding." This idea postulates that brain rhythms organize the otherwise massive number of possible outcomes that could result from, say, 1,000 neurons engaging in independent spiking activity. Instead of all the many combinatorial possibilities, many fewer "subspaces" of activity actually arise, because neurons are coordinated, rather than independent. It is as if the spiking of neurons is like a flock of birds coordinating their movements. Different phases and frequencies of brain rhythms provide this coordination, aligned to amplify each other, or offset to prevent interference. For instance, if a piece of sensory information needs to be remembered, neural activity representing it can be protected from interference when new sensory information is perceived.

"Thus the organization of neural responses into subspaces can both segregate and integrate information," the authors write.

The power of brain rhythms to coordinate and organize information processing in the brain is what enables functional cognition to emerge at that scale, the authors write. Understanding cognition in the brain, therefore, requires studying rhythms.

"Studying individual neural components in isolation -- individual neurons and synapses -- has made enormous contributions to our understanding of the brain and remains important," the authors conclude. "However, it's becoming increasingly clear that, to fully capture the brain's complexity, those components must be analyzed in concert to identify, study, and relate their emergent properties."

• Nervous System
• Brain Tumor
• Psychology Research
• Birth Defects
• Brain-Computer Interfaces
• Neuroscience
• Intelligence
• Brain Injury
• Multiple sclerosis
• Personality disorder
• Mirror neuron
• Functional neuroimaging
• Social cognition

Story Source:

Materials provided by Picower Institute at MIT . Note: Content may be edited for style and length.

Journal Reference :

• Earl K Miller, Scott L Brincat, Jefferson E Roy. Cognition is an emergent property . Current Opinion in Behavioral Sciences , 2024; 57: 101388 DOI: 10.1016/j.cobeha.2024.101388

Cite This Page :

Explore More

• Two Species Interbreeding Created New Butterfly
• Warming Antarctic Deep-Sea and Sea Level Rise
• Octopus Inspires New Suction Mechanism for ...
• Cities Sinking: Urban Populations at Risk
• Puzzle Solved About Ancient Galaxy
• How 3D Printers Can Give Robots a Soft Touch
• Combo of Multiple Health Stressors Harming Bees
• Methane Emission On a Cold Brown Dwarf
• Remarkable Memories of Mountain Chickadees
• Predicting Future Marine Extinctions

Trending Topics

Strange & offbeat.