cornell computational biology phd

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The Department of Computational Biology consists of faculty members with expertise in computer science, genomics, systems biology, population genetics and modeling.  They apply these skills to a wide range of exciting problems in the life sciences.

The department administers the Computational Biology undergraduate concentration within the  Bachelor of Science degree in Biology .  Additionally, the faculty in the department are members of the  Computational Biology graduate field , as well as several other graduate fields offering  M.Sc. and Ph.D. degrees .

Related Groups

• Statistical & Data Science
• Department of Mathematics
• Molecular Biology & Genetics
• Operations Research & Information Engineering
• Cornell Population Center
• Cornell Statistical Consulting Unit

Related Fields

• Applied Mathematics
• Genetics, Genomics and Development
• Mathematics
• Operations Research
• Statistical Science
• Computer Science

Comp Bio in the news

• American Indian and Indigenous Studies Program
• Biological and Environmental Engineering

A new study testing the accuracy of existing methods used to predict the genetic variation that cause infertility found that relying on computational or in vitro experiments alone is insufficient.

Weill Institute for Cell and Molecular Biology

Ithaca, new york, computational biology.

Weill Institute faculty member, Haiyuan Yu, is part of the Computational Biology department.

Broadly speaking, computational biology is the application of computer science, statistics, and mathematics to problems in biology. Computational biology spans a wide range of fields within biology, including genomics/genetics, biophysics, cell biology, biochemistry, and evolution. Likewise, it makes use of tools and techniques from many different quantitative fields, including algorithm design, machine learning, Bayesian and frequentist statistics, and statistical physics.

Our major research strengths in Computational Biology are in comparative, evolutionary, and population genomics. Specific problems of interest include the detection of genomic regions underlying complex traits, the detection of positive selection, the evolutionary genomics of plant and animal domestication, the discovery of new human genes, and the identification and characterization of functional noncoding elements in mammals. Our faculty are key members of a strong, active community at Cornell in comparative, evolutionary, and population genomics that spans departments and colleges.

Visit the Computational Biology department’s website!

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Faculty and researchers.

• Carla Gomes
• Giulia Guidi
• Volodymyr Kuleshov
• Kristina Monakhova
• David Shmoys
• Anke van Zuylen

PhD Students

• Wenxin Chen
• Raunak Kumar
• Acclaimed science writer, Philip Ball discusses Computational Sustainability in a new article in Nature Materials
• AI Reveals Scale of Eelgrass Vulnerability to Warming and Disease: New Research from Carla Gomes
• Carla Gomes and Cornell Researchers Herald Promise of Joining Computational Sustainability with Materials Science
• Carla Gomes and Research Team Use Deep Reasoning Networks to Speed Materials Discovery
• Carla Gomes Explains Impact of Grant to Accelerate the Discovery and Design of Materials via AI
• Graduate program in Computational Biology
• Tri-institutional Program in Computational Biology and Medicine

Related Links

• Institute for Computational Sustainability

The recent completion of the human genome project underlines the need for new computational and theoretical tools in modern biology. The tools are essential for analyzing, understanding and manipulating the detailed information on life we now have at our disposal.

Problems in computational molecular biology vary from understanding sequence data to the analysis of protein shapes, prediction of biological function, study of gene networks, and cell-wide computations.

Cornell has a university-wide plan in the science of genomics; the Department of Computer Science is playing a critical role in this initiative. Researchers in the computer science department are engaged in a wide range of computational biology projects, from genetic mapping, to advanced sequence analysis, fold prediction, structure comparison algorithms, protein classification, comparative genomics, and long-time simulation of protein molecules.

Carla Gomes works on solutions to hard combinatorial problems, with an emphasis on planning and scheduling problems, combining techniques fromm Computer Science (CS), Artificial Intelligence (AI), and Operations Reserach (OR). Her research is leading to the creation of the new field of computational sustainability, which develops and applies computational methods to enable a sustainable environment, economy and society.

Volodymyr Kuleshov 's research focuses on machine learning and its applications in health, personalized medicine, and genomics. His work includes the development of new sequencing technologies powered by machine learning and the creation of machine reading systems for biomedical and scientific literature. He also works on core machine learning problems, such as probabilistic methods, deep generative models, uncertainty estimation, and approximate inference.

Haiyuan Yu performs research research in the broad area of Biomedical Systems Biology with both high-throughput experimental (see Yu et al., Science 2008 ) and integrative computational (see Wang et al., Nature Biotechnology 2012 ) methodologies, aiming to understand gene functions and their relationships within complex molecular networks and how perturbations to such systems may lead to various human diseases. The complexity of biological systems calls for building experimentally-verified computational models based on high-quality large-scale datasets, which is truly the future of biomedical research and the main theme of the lab.

Giulia Guidi  works on challenges at the intersection of large-scale computational biology and the algorithms and software infrastructures. She leverages high-performance computing techniques and computer systems and architectures to make genomics data processing faster and more flexible for the community, and to enable higher-quality bioinformatics and biomedical research. Another important focus of her work is developing software to make cloud infrastructures more accessible for high-performance scientific computing.

Jaehee Kim 's research interests are in the general fields of population-genetic dynamical systems, statistical genetics, and mathematical phylogenetics. She also applies mechanistic understanding of population genetics and evolutionary biology to solve important questions in mathematical epidemiology and forensic genetics.

A new graduate program in Computational Molecular Biology that crosses colleges was initiated with the participation of the computer science field.

/images/cornell/logo35pt_cornell_white.svg" alt="cornell computational biology phd"> Cornell University --> Graduate School

Download the Fields of Study (PDF) .

Fields of Study – Graduate Catalog

Aerospace engineering.

• Aerospace Engineering (M.Eng.) (Distance, Hybrid, or Online Option)
• Aerospace Engineering (M.Eng.) (Ithaca)
• Aerospace Engineering (Ph.D.) (Ithaca)

Africana Studies

• Africana Studies (Ph.D.) (Ithaca)

American Indian and Indigenous Studies (minor)

American studies (minor), animal science.

• Animal Science (M.P.S.) (Ithaca)
• Animal Science (M.S.) (Ithaca)
• Animal Science (M.S./Ph.D.) (Ithaca)
• Animal Science (Ph.D.) (Ithaca)

Anthropology

• Anthropology (Ph.D.) (Ithaca)

Applied Economics and Management

• Applied Economics and Management (M.P.S.) (Ithaca)
• Applied Economics and Management (M.S.) (Ithaca)
• Applied Economics and Management (Ph.D.) (Ithaca)

Applied Information Systems

• Information Systems (M.S.) (Cornell Tech (NYC))

Applied Mathematics

• Applied Mathematics (Ph.D.) (Ithaca)

Applied Physics

• Engineering Physics (M.Eng.) (Ithaca)
• Applied Physics (M.S.) (Ithaca)
• Applied Physics (Ph.D.) (Ithaca)

Archaeology

• Archaeology (M.A.) (Ithaca)

Architecture

• Master of Architecture [Administered by A.A.P] (M.Arch) (Ithaca)
• Advanced Architectural Design (Post-Professional) [Administered by A.A.P] (M.S.) (Ithaca)
• Advanced Urban Design (Post-Professional) [Administered by A.A.P] (M.S.) (Ithaca)
• Architectural Science (M.S.) (Ithaca)
• Design Technology (M.S.) (Ithaca)
• History of Architecture and Urban Development (Ph.D.) (Ithaca)
• Creative Visual Arts [Administered by the College of Architecture, Art, and Planning] (M.F.A.) (Ithaca)
• Image Text, M.S. [Administered by the College of Architecture, Art, and Planning] (M.F.A.) (Ithaca)

Asian American Studies (minor)

Asian literature, religion and culture.

• Asian Literature, Religion, and Culture (Ph.D.) (Ithaca)

Asian Studies

• Asian Studies (M.A.) (Ithaca)

Astronomy and Space Sciences

• Astronomy and Space Sciences (Ph.D.) (Ithaca)

Atmospheric Science

• Atmospheric Sciences (M.S.) (Ithaca)
• Atmospheric Sciences (M.S./Ph.D.) (Ithaca)
• Atmospheric Sciences (Ph.D.) (Ithaca)

Biochemistry, Molecular and Cell Biology

• Biochemistry (Ph.D.) (Ithaca)
• Molecular and Cell Biology (Ph.D.) (Ithaca)

Biological and Environmental Engineering

• Biological and Environmental Engineering (M.Eng.) (Ithaca)
• Agriculture and Life Sciences (Biological and Environmental Engineering) (M.P.S.) (Ithaca)
• Biological and Environmental Engineering (M.S.) (Ithaca)
• Biological and Environmental Engineering (Ph.D.) (Ithaca)

Biomedical Engineering

• Biomedical Engineering (M.Eng.) (Ithaca)
• Biomedical Engineering (Ph.D.) (Ithaca)

Biomedical and Biological Sciences

• Biomedical Sciences (Ph.D.) (Ithaca)
• Biophysics (Ph.D.) (Ithaca)

Chemical Engineering

• Chemical Engineering (M.Eng.) (Ithaca)
• Chemical Engineering (M.S.) (Ithaca)
• Chemical Engineering (Ph.D.) (Ithaca)

Chemistry and Chemical Biology

• Chemistry and Chemical Biology (M.S.) (Ithaca)
• Chemistry and Chemical Biology (Ph.D.) (Ithaca)

City and Regional Planning

• Historic Preservation Planning (M.A.) (Ithaca)
• City and Regional Planning (M.R.P.) (Ithaca)
• City and Regional Planning (M.R.P./M.L.A. (dual degree)) (Ithaca)
• City and Regional Planning (Ph.D.) (Ithaca)

Civil and Environmental Engineering

• Civil and Environmental Engineering (M.Eng.) (Ithaca)
• Engineering Management (M.Eng.) (Distance, Hybrid, or Online Option)
• Engineering Management (M.Eng.) (Ithaca)
• Civil and Environmental Engineering (M.S.) (Ithaca)
• Civil and Environmental Engineering (M.S./Ph.D.) (Ithaca)
• Civil and Environmental Engineering (Ph.D.) (Ithaca)
• Classics (M.A.) (Ithaca)
• Classics (Ph.D.) (Ithaca)

Cognitive Science (minor)

Communication.

• Communication (Ph.D.) (Ithaca)

Comparative Biomedical Sciences

• Veterinary Medical Sciences (M.P.S.) (Distance, Hybrid, or Online Option)

Comparative Literature

• Comparative Literature (Ph.D.) (Ithaca)

Computational Biology

• Computational Biology (Ph.D.) (Ithaca)

Computational Science and Engineering (minor)

Computer science.

• Computer Science (M.Eng.) (Cornell Tech (NYC))
• Computer Science (M.Eng.) (Ithaca)
• Computer Science (M.S.) (Ithaca)
• Computer Science (Ph.D.) (Ithaca)

Data Science (minor)

Demography (minor), design and environmental analysis.

• Design (M.A.) (Ithaca)
• Human-Environment Relations (M.S.) (Ithaca)
• Human Behavior and Design (Ph.D.) (Ithaca)

Development Studies

• Development Sociology (Ph.D.) (Ithaca)

Ecology and Evolutionary Biology

• Ecology (M.S.) (Ithaca)
• Evolutionary Biology (M.S.) (Ithaca)
• Ecology (Ph.D.) (Ithaca)
• Evolutionary Biology (Ph.D.) (Ithaca)
• Economics (Ph.D.) (Ithaca)

Electrical and Computer Engineering

• Electrical and Computer Engineering (M.Eng.) (Cornell Tech (NYC))
• Electrical and Computer Engineering (M.Eng.) (Ithaca)
• Electrical and Computer Engineering (Ph.D.) (Ithaca)

English Language and Literature

• Creative Writing (M.F.A.) (Ithaca)
• English Language and Literature (Ph.D.) (Ithaca)
• Entomology (M.S.) (Ithaca)
• Entomology (Ph.D.) (Ithaca)

Environmental Quality (minor)

Epidemiology (minor), feminist, gender, and sexuality studies (minor), fiber science and apparel design.

• Apparel Design (M.A.) (Ithaca)
• Human Ecology (M.P.S.) (Ithaca)
• Fiber Science (M.S.) (Ithaca)
• Apparel Design (Ph.D.) (Ithaca)
• Fiber Science (Ph.D.) (Ithaca)

Film and Video Studies (minor)

Food science and technology.

• Food Science and Technology (M.F.S.) (Ithaca)
• Food Science and Technology (M.S.) (Ithaca)
• Food Science and Technology (Ph.D.) (Ithaca)

Genetics, Genomics and Development

• Genetics (Ph.D.) (Ithaca)

Genomics (minor)

Geological sciences.

• Geological Sciences (M.Eng.) (Ithaca)
• Geological Sciences (M.S.) (Ithaca)
• Geological Sciences (Ph.D.) (Ithaca)

Germanic Studies

• Germanic Studies (Ph.D.) (Ithaca)

Global Development

• Global Development (M.P.S.) (Ithaca)
• Government (Ph.D.) (Ithaca)
• History (Ph.D.) (Ithaca)

History of Art, Archaeology, and Visual Studies

• History of Art and Archaeology (Ph.D.) (Ithaca)

Horticulture

• Horticultural Biology (M.S.) (Ithaca)
• Horticultural Biology (Ph.D.) (Ithaca)

Hotel Administration

• Hospitality Management (M.M.H.) (Ithaca)
• Hotel Administration (M.S.) (Ithaca)
• Hotel Administration (Ph.D.) (Ithaca)

Human Development

• Developmental Psychology (M.A.) (Ithaca)
• Human Development and Family Studies (M.A.) (Ithaca)
• Developmental Psychology (Ph.D.) (Ithaca)
• Human Development and Family Studies (Ph.D.) (Ithaca)

Industrial and Labor Relations

• Industrial and Labor Relations (M.I.L.R.) (Ithaca)
• Industrial and Labor Relations (M.I.L.R./M.B.A. (dual degree)) (Ithaca)
• Human Resources (Executive Master of Human Resource Management) (M.P.S.) (Distance, Hybrid, or Online Option)
• Industrial and Labor Relations (M.P.S.) (Ithaca)
• Industrial and Labor Relations (M.S.) (Ithaca)
• Industrial and Labor Relations (M.S./Ph.D.) (Ithaca)

Information Science

• Information Science (M.P.S.) (Ithaca)
• Information Science (Ph.D.) (Ithaca)

Integrative Plant Science

• Integrative Plant Science (M.P.S.) (Ithaca)

Landscape Architecture

• Landscape Architecture (M.L.A.) (Ithaca)
• Landscape Architecture (M.P.S.) (Ithaca)
• Landscape Architecture (M.R.P./M.L.A. (dual degree)) (Ithaca)

Latin American Studies (minor)

Latina/o studies (minor).

• Law (J.S.D) (Ithaca)

Lesbian, Gay, Bisexual and Transgender Studies (minor)

Linguistics.

• Linguistics (Ph.D.) (Ithaca)
• Management (M.P.S.) (Ithaca)
• Management (Ph.D.) (Ithaca)

Materials Science and Engineering

• Materials Science and Engineering (M.Eng.) (Ithaca)
• Materials Science and Engineering (M.S.) (Ithaca)
• Materials Science and Engineering (Ph.D.) (Ithaca)

Mathematics

• Mathematics (Ph.D.) (Ithaca)

Mechanical Engineering

• Mechanical Engineering (M.Eng.) (Distance, Hybrid, or Online Option)
• Mechanical Engineering (M.Eng.) (Ithaca)
• Mechanical Engineering (M.S.) (Ithaca)
• Mechanical Engineering (Ph.D.) (Ithaca)

Media Studies (minor)

Medieval studies.

• Medieval Studies (Ph.D.) (Ithaca)

Microbiology

• Microbiology (Ph.D.) (Ithaca)
• Music (D.M.A.) (Ithaca)
• Music (Ph.D.) (Ithaca)

Natural Resources and the Environment

• Agriculture and Life Sciences (Natural Resources) (M.P.S.) (Ithaca)
• Natural Resources (M.S.) (Ithaca)
• Natural Resources (M.S./Ph.D.) (Ithaca)
• Natural Resources (Ph.D.) (Ithaca)

Near Eastern Studies

• Near Eastern Studies (Ph.D.) (Ithaca)

Neurobiology and Behavior

• Behavioral Biology (Ph.D.) (Ithaca)
• Neurobiology (Ph.D.) (Ithaca)
• Nutrition (M.S.) (Ithaca)
• Nutrition (Ph.D.) (Ithaca)

Operations Research and Information Engineering

• Operations Research and Information Engineering (M.Eng.) (Cornell Tech (NYC))
• Operations Research and Information Engineering (M.Eng.) (Ithaca)
• Operations Research (Ph.D.) (Ithaca)

Peace Studies and Peace Science (minor)

Performing and media arts.

• Performing and Media Arts (Ph.D.) (Ithaca)
• Philosophy (Ph.D.) (Ithaca)
• Physics (Ph.D.) (Ithaca)

Plant Biology

• Plant Biology (Ph.D.) (Ithaca)

Plant Breeding

• Plant Breeding (M.S.) (Ithaca)
• Plant Breeding (M.S./Ph.D.) (Ithaca)
• Plant Breeding (Ph.D.) (Ithaca)

Plant Pathology and Plant-Microbe Biology

• Plant Pathology (M.S.) (Ithaca)
• Plant Pathology (Ph.D.) (Ithaca)
• Psychology (Ph.D.) (Ithaca)

Public Affairs

• Public Administration (M.H.A./M.P.A. (dual degree)) (Ithaca)
• Executive Masters of Public Administration (M.P.A.) (Distance, Hybrid, or Online Option)
• Public Administration (M.P.A.) (Ithaca)

Public Health and Planetary Health

• Public Health (Master of Public Health) (Ithaca)

Public Policy

• Executive Health Administration (M.H.A.) (Distance, Hybrid, or Online Option)
• Health Administration (M.H.A.) (Ithaca)
• Health Administration (M.H.A./M.P.A. (dual degree)) (Ithaca)
• Public Policy (Ph.D.) (Ithaca)

Real Estate

• Real Estate (M.P.S.( R.E.)) (Ithaca)

Regional Science

• Regional Science (M.A.) (Ithaca)
• Regional Science (M.S.) (Ithaca)
• Regional Science (Ph.D.) (Ithaca)

Religious Studies (minor)

Risk analysis, communication and policy (minor), romance studies.

• Romance Studies (Ph.D.) (Ithaca)

Science and Technology Studies

• Science and Technology Studies (Ph.D.) (Ithaca)
• Sociology (Ph.D.) (Ithaca)

Soil and Crop Sciences

• Soil and Crop Sciences (M.S.) (Ithaca)
• Soil and Crop Sciences (M.S./Ph.D.) (Ithaca)
• Soil and Crop Sciences (Ph.D.) (Ithaca)
• Applied Statistics (M.P.S. (A.S.)) (Ithaca)
• Statistics (Ph.D.) (Ithaca)

Stem Cell Biology (minor)

Sustainable energy (minor), systems engineering.

• Systems Engineering (M.Eng.) (Distance, Hybrid, or Online Option)
• Systems Engineering (M.Eng.) (Ithaca)
• Systems (M.S.) (Ithaca)
• Systems (Ph.D.) (Ithaca)

Theoretical and Applied Mechanics

• Theoretical and Applied Mechanics (Ph.D.) (Ithaca)

Urban Studies (minor)

Water resources (minor), narrow your search.

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Why should you consider Cornell?

Genomics and Computational Biology

A Genetic Locus Influencing DNA Replication Timing in Humans (Amnon Koren)

Students in this area use cutting-edge wet lab and computational methods to understand the functional significance of genome-scale variation and genome organization. For example, some groups in this area are developing sophisticated computational models to understand genome-scale variation of DNA, as well as integrating it with large data sets of phenotypic information. Other groups are identifying and annotating functional elements such as enhancers that impact gene expression.

Research Area Faculty

Graduate Field of Genetics, Genomics and Development 526 Campus Road Biotechnology Building Cornell University Ithaca, NY 14853 gendev@cornell.edu

If you have a disability and are having trouble accessing information on this website or need materials in an alternate format, contact  web-accessibility@cornell.edu  for assistance.

Alumni spotlight: Nora Brown, Ph.D. ’23

By | Staff , Cornell University Graduate School

Nora Brown, Ph.D. ’23, is an alumna of the genetics, genomics, and development doctoral program at Cornell, during which she was co-advised by Mariana Wolfner and Andrew Clark. She is now a postdoc at MIT.

What was your research focus at Cornell?

My thesis work was focused on understanding the function and evolution of a major class of male reproductive proteins called seminal fluid proteins, which are important for fertility in many species (including humans). Using fruit flies as a model organism, I uncovered a novel role in reproduction for a set of seminal proteins (called odorant binding proteins) that normally function in the fly’s ability to smell and taste, but in the seminal fluid they act to form a “mating plug” that helps store sperm after mating. I also used a comparative genomics approach to show how these proteins can change rapidly over evolutionary time.

What is the broader implication of this research and how is it making an impact?

Seminal fluid proteins are quite different across distantly related species (they are similar to immune genes that evolve rapidly), but they often show similarities at the functional level. Odorant binding proteins are very often found in the seminal fluid in insects, and my research lays the groundwork for testing whether the mating plug function is shared or unique to particular species. More broadly speaking, understanding the functions of insect reproductive proteins can lead to better approaches for controlling human disease vectors like mosquitos and can even provide insight into human infertility. 

Tell us about your current position and what you find rewarding about it.

I am currently a postdoctoral associate in Yukiko Yamashita’s lab in the Whitehead Institute at MIT working on the genetics and evolution of sperm development in flies. While my thesis work also focused on male fertility and ejaculate molecules, I really studied everything except ​ the sperm, so I’m excited for a new challenge and excited to join a vibrant field that’s rooted deep within the history of Drosophila genetics. Coming from a functional and evolutionary genetics background, it’s also been really rewarding learning from my lab mates and advisor on totally new topics and techniques to me.

What does it mean to you to have been selected as a finalist for the SUNY Chancellor’s Dissertation Award?

I’m very grateful and honored to have been selected as a finalist for this award among all of the amazing researchers in the SUNY community. This recognition means a lot, as I think a lot of fundamental biology can be better understood (or even discovered) through an evolutionary lens. Understanding how evolution can shape reproductive processes can also help bridge research in different fields, leading to novel ideas and perspectives that otherwise might have been overlooked. 

What will this award allow you to do that you may not have otherwise?

Funds from this award will hopefully allow me to attend a conference or courses to develop new skills and connect with other researchers as I embark into a new field.

If you could go back in time to the beginning of your graduate career, what advice would you give yourself?

I would tell myself to cast a wide net for mentor figures—of course your advisor usually ends up being your main mentor, but talking to lots of other PIs/postdocs/senior grad students in your department or field can be extremely helpful in getting feedback or advice or ideas. I would also tell myself to find a crew of trusted peers as quickly as possible—the friends you make in grad school are in the trenches right alongside you, and can be an amazing source of support and advice both in and beyond grad school. 

Read the story on the Cornell University Graduate School website .

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In the Biological Sciences program   .

In addition to the concentration requirements outlined below, all students must complete the Biological Sciences foundation requirements:

• Biological Sciences Foundation Requirements    

Computation methods and data mining has become essential to biological research. Technology for collecting high-throughput data, such as genomic technology, mass spectrometry, and MRI imaging, and the development of large-scale databases, such as those for genomes, epidemiology, and compilations of biological information types, have made available unprecedented amounts of detailed information that require computationally intensive methodologies to access and analyze. These data and computational methods are transforming almost all of biological research. Problems investigated by computational biologists include topics as diverse as the genetics of disease susceptibility; comparing entire genomes to reveal the evolutionary history of life; predicting the structure, motions, and interactions of proteins; designing new therapeutic drugs; modeling the complex signaling mechanisms within cells; predicting how ecosystems will respond to climate change; and designing recovery plans for endangered species. The computational biologist must have skills in mathematics, statistics, machine learning, and the physical sciences as well as in biology. A key goal in training is to develop the ability to relate biological processes to computational models. Cornell faculty work primarily in six subareas of computational biology: 1. computational and statistical genomics, 2. population, comparative, and functional genomics, 3. bioinformatics, 4. proteomics, 5. ecology and evolutionary biology, and 6. statistical and computational methods for modeling biological systems. Beyond core skills in mathematics, physical sciences and biology, the computational biology concentration requires additional coursework in mathematics and computer programming, a “bridging” course aimed at connecting biology to computation, and an advanced course where the theoretical/computational component of one aspect of biology is studied. Students should enroll in the more rigorous courses in the physical and mathematical sciences, and may wish to take additional courses in these areas. Computational biology has applications as broad as biology itself. The problems of interest and the tools available to study them are constantly evolving, so students are encouraged to gain fundamental skills that will serve them throughout their careers. There is great, and increasing, demand for research scientists and technical personnel who can bring mathematical and computational skills to the study of biological problems. This concentration is also an excellent preparation for graduate study in any area of biology or computational biology.

Students are required to select courses from the list below to complete the concentration; however, if students find another relevant course that is not listed, they may petition the Office of Undergraduate Biology .

Computational Biology Requirements:

• All requirements must be taken for letter grade unless the course is offered S/U only. Exceptions to the grading option and any course substitutions must be approved via the biological sciences petition . Students are encouraged to discuss exceptions and course substitutions with their faculty advisor prior to submitting petition.
• A grade of D- or better must be obtained to count course for concentration.
• A minimum of 12 credits of concentration requirements

a. One course in computer programming:

• CS 1110 - Introduction to Computing: A Design and Development Perspective
• CS 1112 - Introduction to Computing: An Engineering and Science Perspective
• CS 2024 - C++ Programming

b. One additional course in mathematics:

• BTRY 3080 - Probability Models and Inference (crosslisted)
• MATH 2210 - Linear Algebra
• MATH 2310 - [Linear Algebra with Applications]
• MATH 2940 - Linear Algebra for Engineers
• MATH 4200 - Differential Equations and Dynamical Systems
• MATH 4210 - Nonlinear Dynamics and Chaos
• MATH 4710 - Basic Probability
• MATH 4740 - Stochastic Processes

c. One of the following bridging courses:

 i.e., a course in mathematical modeling applied to biology:

• BIOCB 4381 - Biomedical Data Mining and Modeling
• BIOCB 4910 - Advanced Population Genetics
• BIOMG 4810 - Population Genetics (crosslisted)
• BIOMG 4870 - Human Genomics
• BIOMG 6310 - Protein Structure and Function
• BIONB 3300 - [Introduction to Computational Neuroscience] (crosslisted)
• BIONB 4220 - [Modeling Behavioral Evolution]
• BIONB 4740 - [Neural Dynamics of Learning, Memory and Decision Making]
• BME 3110 - Cellular Systems Biology
• BIOCB 4830 - Quantitative Genomics and Genetics
• BIOCB 4840 - Computational Genetics and Genomics (crosslisted)
• BIOCB 6910 - Advanced Population Genetics
• ENTOM 4610 - Model-Based Phylogenetics and Hypothesis Testing
• NTRES 3100 - Applied Population Ecology
• NTRES 4100 - Advanced Conservation Biology: Concepts and Techniques
• NTRES 4120 - Wildlife Population Analysis: Techniques and Models
• PLBIO 4000 - Concepts and Techniques in Computational Biology
• PLBIO 4400 - [Phylogenetic Systematics]
• STSCI 4740 - Data Mining and Machine Learning

d. One course from the following list of advanced courses:

 or an additional “bridging” course numbered 4000 or above:

• BTRY 4090 - Theory of Statistics (crosslisted)
• CS 2110 - Object-Oriented Programming and Data Structures (crosslisted)
• CS 4210 - Numerical Analysis and Differential Equations (crosslisted)
• CS 4220 - Numerical Analysis: Linear and Nonlinear Problems (crosslisted)
• CS 4780 - Introduction to Machine Learning
• ORIE 3500 - Engineering Probability and Statistics II
• ORIE 3510 - Introduction to Engineering Stochastic Processes I (crosslisted)
• Many of the “bridging” and “advanced” courses listed above (items c and d) are offered only in alternate years or irregularly, and many have one or more prerequisites that are not required for the biological sciences major or this concentration. Students therefore need to plan well in advance how they will satisfy these requirements, and verify when course offerings will occur.
• It is strongly recommended that students in this concentration use PHYS 2207   / PHYS 2208    to satisfy the core physics requirement.
• It is strongly recommended that students complete the core organic chemistry requirement using the CHEM 1570    option, and that the time saved be used to take either CS 2110    or a second mathematics course from the list above.
• One course may not be used to satisfy two different requirements simultaneously. For example, BTRY 3080    can be used to satisfy either requirement (b) or requirement (d), but not both.
• Students who use BTRY 3080    to fulfill the additional mathematics requirement should not use ORIE 3500 - Engineering Probability and Statistics II    to fulfill the requirement for an advanced course.

AP credit in computer science can be used to satisfy part (a) One course in computer programming.

BIOG 49** courses (4990, independent study, TA, undergrad seminar) cannot be used towards the computational biology concentration.

Computational Biology Concentration Curriculum Learning Objectives

After completing the concentration in Computational Biology, students will be able to:

• Have sufficient background knowledge in mathematics, probability, and statistics to be able to conceptualize key quantitative aspects of biological processes at the cellular, organismal or population level. 
• Have the ability to apply computational and mathematical methods to model and/or analyze biological processes.
• Have acquired working skills in at least one computer programming language to be able to implement modeling approaches.
• Be able to organize, manipulate, and maintain the integrity of large, complex data sets.
• Have developed advanced knowledge in at least one area of computational, statistical, mathematical, and/or quantitative modeling or analysis of data from biology. This includes background knowledge of the current literature in the field, and ability to assess the merits of current literature.

Course Offerings in Computational Biology

A complete list of Biometry and Statistics courses    

Graduate Outcomes - All Majors

First destinations.

• 58% Employment
• 26% Graduate School
• 3% Post-Graduate Service
• 3% Fellowships
• 2% Continuing Education

Top Locations

• Chicago, IL
• Iowa City, IA
• Minneapolis & St. Paul, MN
• New York, NY
• Washington, DC

Employment Data

Positions related to career goals.

• 39% Completely
• 48% Moderately
• 11% Slightly
• 3% Not at All

Graduate Schools

Graduate schools data, graduate school acceptance info, organization.

• Conservation Corps
• Green Iowa AmeriCorps
• Habitat for Humanity
• Las Americas Immigrant Advocacy Center
• Peace Corps
• U.S. Committee for Refugees and Immigrants

Service Data

Fellowships.

• Centers for Disease Control and Prevention
• Coro Fellowship in Public Affairs
• National Institutes of Health
• NYU Shanghai
• The Fulbright Program
• Thomas J. Watson Foundation

Fellowships Data

Experiential learning, internships data, research data.

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MS-CB Curriculum

Curriculum Overview

The first half of the program contains the foundation and competency courses. These courses include the necessary fundamentals in mathematics, biology, and computer science, and provide training in quantitative biology and bioinformatics. The second half of the program is dedicated mainly to thesis research. Students will join one of our top-notch research labs to work on an independent project, develop more specialized expertise, and increase skills in problem solving, critical thinking, and science communication.

Training Timeline

Data Structures and Algorithms for Computational Biology

Dynamic Models in Biology

Cellular and Molecular Biology

MS-CB Thesis Research Exploration 1

Possible Elective

Spring Term

Analysis of Next-Generation Sequencing Data

Functional Interpretation of High-Throughput Data

Quantitative Genomics & Genetics

Career Development in Computational Biology

MS-CB Thesis Research Exploration 2

Responsible Conduct of Research   

Summer Term

MS-CB Thesis Research 1

Fall 2 Term

MS-CB Thesis Research 2

Winter 2 Term (January-February)

MS-CB Thesis Research 3

Possible Electives

Students are required to take at least two electives among program-approved WCGS and Cornell Tech offerings. At least one elective must cover statistical or machine learning. Possibilities include courses on applied machine learning, deep learning, natural language processing, computer vision, AI, and statistical learning. Other electives include courses on biostatistics, health informatics, biomedical entrepreneurship, genomic innovation, etc.

Weill Cornell Medicine Graduate School of Medical Sciences 1300 York Ave. Box 65 New York, NY 10065 Phone: (212) 746-6565 Fax: (212) 746-8906