technical analysis bachelor thesis

Technical Analysis

• First Online: 31 October 2015

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• Prodromos E. Tsinaslanidis 3 &
• Achilleas D. Zapranis 4  

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In this chapter the topic of technical analysis is discussed. Initially, technical analysis and its relation with the efficient market hypothesis are presented. Subsequently, a bundle of celebrated tools, that technicians implement in their trading activities, along with the corresponding, reported in the literature, empirical findings are presented. Particular emphasis is given on the bibliography on technical patterns, since this is the main area that this book examines. Afterwards, this chapter discusses on some controversial perceptions on the characterization and the implementation of technical analysis. At the end of the chapter, an outline of the book is given.

• Abnormal Return
• Excess Return
• Dynamic Time Warping
• Trading Rule
• Price Series

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He was the first one to publish a stock market average in 1884. This average was actually the first market index and included 11 stocks. In 1987 the initial index was separated into two indices. The first one included 12 stocks from the industry sector and the second one 20 stocks from the rail sector. For more information concerning Dow’s life and the six tenets of the Dow theory see Edwards and Magee ( 1997 ).

See martingale model discussed later in this section.

We use the natural logarithm of prices instead of the actual prices, because in the latter case, when assuming normal distribution for the variable \( {P}_t \) , there is always a positive probability to observe a negative price, which is not possible.

\( cov \) stands for covariance. For a more detailed and comprehensive description of the three versions of random walk hypothesis along with their corresponding tests, see Campbell et al. ( 1997 ).

This type of SAR levels is identified from minima or maxima realized in a precedent, constant time intervals (see Chap. 4 ).

Percentage envelopes (or volatility bands) and high-low bands are other types of filters described in (Murphy 1986 ) but their use cannot be generalized to other technical trading rules to the extent that filters, we have already presented, do.

The reader can see the survey-based articles of (Menkhoff 1997 ; Taylor and Allen 1992 ) where this attribution of TA is examined.

The term “ self-fulfilling prophecy ” stems from the Thomas Theorem which was coined in 1928. According to this theorem “ if men define situations as real, they are real in their consequences ”. For a comprehensive discussion on this theorem, see Merton ( 1995 ) and references therein.

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Tsinaslanidis, P.E., Zapranis, A.D. (2016). Technical Analysis. In: Technical Analysis for Algorithmic Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-23636-0_1

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How do I make a data analysis for my bachelor, master or PhD thesis?

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• Knowledge of generative models and their basics (e.g., Normalizing Flows, Diffusion Models, VAE)
• Optional: Neural ODEs/SDEs, Optimal Transport, Measure Theory

With recent advances, such as Diffusion Models, Transformers, Normalizing Flows, Flow Matching, etc., the field of generative models has gained significant attention in the machine learning and artificial intelligence research community. However, many problems and questions remain open, and the application to complex data domains such as graphs, time series, point processes, and sets is often non-trivial. We are interested in supervising motivated students to explore and extend the capabilities of state-of-the-art generative models for various data domains.

Contact : Marcel Kollovieh , David Lüdke

• Flow Matching for Generative Modeling
• Auto-Encoding Variational Bayes
• Denoising Diffusion Probabilistic Models 
• Structured Denoising Diffusion Models in Discrete State-Spaces

Graph Structure Learning

Type:  Guided Research / Hiwi

• Optional: Knowledge of graph theory and mathematical optimization

Graph deep learning is a powerful ML concept that enables the generalisation of successful deep neural architectures to non-Euclidean structured data. Such methods have shown promising results in a vast range of applications spanning the social sciences, biomedicine, particle physics, computer vision, graphics and chemistry. One of the major limitations of most current graph neural network architectures is that they often rely on the assumption that the underlying graph is known and fixed. However, this assumption is not always true, as the graph may be noisy or partially and even completely unknown. In the case of noisy or partially available graphs, it would be useful to jointly learn an optimised graph structure and the corresponding graph representations for the downstream task. On the other hand, when the graph is completely absent, it would be useful to infer it directly from the data. This is particularly interesting in inductive settings where some of the nodes were not present at training time. Furthermore, learning a graph can become an end in itself, as the inferred structure can provide complementary insights with respect to the downstream task. In this project, we aim to investigate solutions and devise new methods to construct an optimal graph structure based on the available (unstructured) data.

Contact : Filippo Guerranti

• A Survey on Graph Structure Learning: Progress and Opportunities
• Differentiable Graph Module (DGM) for Graph Convolutional Networks
• Learning Discrete Structures for Graph Neural Networks

NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification

A Machine Learning Perspective on Corner Cases in Autonomous Driving Perception  

Type: Master's Thesis 

Industrial partner: BMW 

Prerequisites: 

• Strong knowledge in machine learning 
• Knowledge of Semantic Segmentation  
• Good programming skills 
• Proficiency with Python and deep learning frameworks (TensorFlow or PyTorch) 

Description: 

In autonomous driving, state-of-the-art deep neural networks are used for perception tasks like for example semantic segmentation. While the environment in datasets is controlled in real world application novel class or unknown disturbances can occur. To provide safe autonomous driving these cased must be identified. 

The objective is to explore novel class segmentation and out of distribution approaches for semantic segmentation in the context of corner cases for autonomous driving. 

Contact: Sebastian Schmidt

References: 

• Segmenting Known Objects and Unseen Unknowns without Prior Knowledge 
• Efficient Uncertainty Estimation for Semantic Segmentation in Videos  
• Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family  
• Description of Corner Cases in Automated Driving: Goals and Challenges 

Active Learning for Multi Agent 3D Object Detection 

Type: Master's Thesis  Industrial partner: BMW 

• Knowledge in Object Detection 
• Excellent programming skills 

In autonomous driving, state-of-the-art deep neural networks are used for perception tasks like for example 3D object detection. To provide promising results, these networks often require a lot of complex annotation data for training. These annotations are often costly and redundant. Active learning is used to select the most informative samples for annotation and cover a dataset with as less annotated data as possible.   

The objective is to explore active learning approaches for 3D object detection using combined uncertainty and diversity based methods.  

• Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving   
• Efficient Uncertainty Estimation for Semantic Segmentation in Videos   
• KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
• Towards Open World Active Learning for 3D Object Detection   

Graph Neural Networks

Type:  Master's thesis / Bachelor's thesis / guided research

• Knowledge of graph/network theory

Graph neural networks (GNNs) have recently achieved great successes in a wide variety of applications, such as chemistry, reinforcement learning, knowledge graphs, traffic networks, or computer vision. These models leverage graph data by updating node representations based on messages passed between nodes connected by edges, or by transforming node representation using spectral graph properties. These approaches are very effective, but many theoretical aspects of these models remain unclear and there are many possible extensions to improve GNNs and go beyond the nodes' direct neighbors and simple message aggregation.

Contact: Simon Geisler

• Semi-supervised classification with graph convolutional networks
• Relational inductive biases, deep learning, and graph networks
• Diffusion Improves Graph Learning
• Weisfeiler and leman go neural: Higher-order graph neural networks
• Reliable Graph Neural Networks via Robust Aggregation

Physics-aware Graph Neural Networks

Type:  Master's thesis / guided research

• Proficiency with Python and deep learning frameworks (JAX or PyTorch)
• Knowledge of graph neural networks (e.g. GCN, MPNN, SchNet)
• Optional: Knowledge of machine learning on molecules and quantum chemistry

Deep learning models, especially graph neural networks (GNNs), have recently achieved great successes in predicting quantum mechanical properties of molecules. There is a vast amount of applications for these models, such as finding the best method of chemical synthesis or selecting candidates for drugs, construction materials, batteries, or solar cells. However, GNNs have only been proposed in recent years and there remain many open questions about how to best represent and leverage quantum mechanical properties and methods.

Contact: Nicholas Gao

• Directional Message Passing for Molecular Graphs
• Neural message passing for quantum chemistry
• Learning to Simulate Complex Physics with Graph Network
• Ab initio solution of the many-electron Schrödinger equation with deep neural networks
• Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
• Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Robustness Verification for Deep Classifiers

Type: Master's thesis / Guided research

• Strong machine learning knowledge (at least equivalent to IN2064 plus an advanced course on deep learning)
• Strong background in mathematical optimization (preferably combined with Machine Learning setting)
• Proficiency with python and deep learning frameworks (Pytorch or Tensorflow)
• (Preferred) Knowledge of training techniques to obtain classifiers that are robust against small perturbations in data

Description : Recent work shows that deep classifiers suffer under presence of adversarial examples: misclassified points that are very close to the training samples or even visually indistinguishable from them. This undesired behaviour constraints possibilities of deployment in safety critical scenarios for promising classification methods based on neural nets. Therefore, new training methods should be proposed that promote (or preferably ensure) robust behaviour of the classifier around training samples.

Contact: Aleksei Kuvshinov

References (Background):

• Intriguing properties of neural networks
• Explaining and harnessing adversarial examples
• SoK: Certified Robustness for Deep Neural Networks
• Certified Adversarial Robustness via Randomized Smoothing
• Formal guarantees on the robustness of a classifier against adversarial manipulation
• Towards deep learning models resistant to adversarial attacks
• Provable defenses against adversarial examples via the convex outer adversarial polytope
• Certified defenses against adversarial examples
• Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks

Uncertainty Estimation in Deep Learning

Type: Master's Thesis / Guided Research

• Strong knowledge in probability theory

Safe prediction is a key feature in many intelligent systems. Classically, Machine Learning models compute output predictions regardless of the underlying uncertainty of the encountered situations. In contrast, aleatoric and epistemic uncertainty bring knowledge about undecidable and uncommon situations. The uncertainty view can be a substantial help to detect and explain unsafe predictions, and therefore make ML systems more robust. The goal of this project is to improve the uncertainty estimation in ML models in various types of task.

Contact: Tom Wollschläger ,   Dominik Fuchsgruber ,   Bertrand Charpentier

• Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
• Predictive Uncertainty Estimation via Prior Networks
• Posterior Network: Uncertainty Estimation without OOD samples via Density-based Pseudo-Counts
• Evidential Deep Learning to Quantify Classification Uncertainty
• Weight Uncertainty in Neural Networks

Hierarchies in Deep Learning

Type:  Master's Thesis / Guided Research

Multi-scale structures are ubiquitous in real life datasets. As an example, phylogenetic nomenclature naturally reveals a hierarchical classification of species based on their historical evolutions. Learning multi-scale structures can help to exhibit natural and meaningful organizations in the data and also to obtain compact data representation. The goal of this project is to leverage multi-scale structures to improve speed, performances and understanding of Deep Learning models.

Contact: Marcel Kollovieh , Bertrand Charpentier

• Tree Sampling Divergence: An Information-Theoretic Metricfor Hierarchical Graph Clustering
• Hierarchical Graph Representation Learning with Differentiable Pooling
• Gradient-based Hierarchical Clustering
• Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space

• Hauptnavigation
• Unternavigation

Institute for Software Systems

Student Theses

Randomized Generation of Flaky Test Suites . Bachelor Thesis, TU Hamburg, January 2024 BibTex Entry   Paper(PDF)

Iterative Neural Network Optimization Using the Apricot Weight-Adaption Approach with Weighted Training Subsets . Bachelor Thesis, TU Hamburg, January 2024 BibTex Entry   Paper(PDF)

Mutation-Based Accuracy Improvements in Neural Networks using Spectrum-Based Fault Localization . Bachelor Thesis, TU Hamburg, January 2024 BibTex Entry   Paper(PDF)

User Partitioning for Anytime Local-Search MaxSAT Solvers . Bachelor Thesis, TU Hamburg, October 2023 BibTex Entry   Paper(PDF)

Attacking Defense Strategies of Neural Networks using Dynamic Backdoors . Master Thesis, TU Hamburg, August 2023 BibTex Entry   Paper(PDF)

Braille Translation using Stochastic Parsing . Bachelor Thesis, TU Hamburg, July 2023 BibTex Entry   Paper(PDF)

Enhancing the Fix Patterns Database of Static Analysis Violations in Automated Semantic Program Repair using AVATAR . Bachelor Thesis, TU Hamburg, July 2023 BibTex Entry   Paper(PDF)

Application of Frequency Fitness Assignment for solving MaxSAT problems . Project Thesis, TU Hamburg, July 2023 BibTex Entry   Paper(PDF)

Comparison and Implementation of Graph Traversal Algorithms for Automating Model-Based GUI Testing . Bachelor Thesis, TU Hamburg, July 2023 BibTex Entry   Paper(PDF)

Conformance Testing in UPPAAL with First and Higher-Order Mutants for Timed Automata . Master Thesis, TU Hamburg, June 2023 BibTex Entry   Paper(PDF)

Scalability of a Noise Based Logic Simulation for Solving SAT . Bachelor Thesis, TU Hamburg, June 2023 BibTex Entry   Paper(PDF)

Ensemble Learning of Neural Networks and Time-Series . Project Thesis, TU Hamburg, May 2023 BibTex Entry   Paper(PDF)

Automated Translation of Partially Stochastic Time Petri Nets for Use with UPPAAL Model Checkers . Project Thesis, TU Hamburg, April 2023 BibTex Entry   Paper(PDF)

Ein MaxSAT-basierter Ansatz zum Lernen linearer temporaler Eigenschaften für Online-Systeme. Bachelor Thesis, TU Hamburg, February 2023 BibTex Entry   Paper(PDF)

Transfer Learning Code Smells using Version History. Master Thesis, TU Hamburg, January 2023 BibTex Entry   Paper(PDF)

Conflict-based Path Planning for Multiple Autonomous Agent. Project Thesis, TU Hamburg, January 2023 BibTex Entry   Paper(PDF)

Infiltration of Deep Neural Networks using Dynamic Backdoors. Project Thesis, TU Hamburg, November 2022 BibTex Entry   Paper(PDF)

Automating Statistical pWCET Analysis for Strategy Synthesis in UPPAAL. Project Thesis, TU Hamburg, November 2022 BibTex Entry   Paper(PDF)

A recursive learning algorithm for one-clock timed automata. Bachelor Thesis, TU Hamburg, September 2022 BibTex Entry   Paper(PDF )

Implementation and Optimization of Statement-Level Test-Case Minimization . Bachelor Thesis, TU Hamburg, July 2022 BibTex Entry   Paper(PDF)

On-the-Fly Strategy Synthesis for Timed-Game Automata with Logically Conjunctive Reachability and Safety Properties . Master Thesis, TU Hamburg, July 2022 BibTex Entry   Paper(PDF)

On-the-fly Model Checking for Kripke Structures using Dependency Graphs . Bachelor Thesis, TU Hamburg, May 2022 BibTex Entry   Paper(PDF)

Kripke Model Minimisation under CTL-Formula Constraints . Bachelor Thesis, TU Hamburg, May 2022 BibTex Entry   Paper(PDF)

Optimization of Machine Learning Algorithms for Network Intrusion Detection . Bachelor Thesis, TU Hamburg, March 2022 BibTex Entry   Paper(PDF)

Identifying Bug-Introducing Changes using Test Coverage and Information Retrieval Techniques . Bachelor Thesis, TU Hamburg, March 2022 BibTex Entry   Paper(PDF)

Herleitung der WCET für PRET-Maschinen mittels Bounded Model Checking . Bachelor Thesis, TU Hamburg, February 2022 BibTex Entry   Paper(PDF)

Learning UPPAAL Timed Automata from Network Protocol Traces . Bachelor Thesis, February 2022 BibTex Entry   Paper(PDF)

Detecting failures of machine learning services by black-box attacks. Bachelor Thesis, TU Hamburg, February 2022 BibTex Entry    Paper(PDF)

Performance prediction of reachability queries over a large knowledge graph. Bachelor Thesis, TU Hamburg, January 2022 BibTex Entry    Paper(PDF)

System Performance Modeling Based on the Combination of Property-Based Testing and Multiple Linear Regression. Bachelor Thesis, TU Hamburg, January 2022 BibTex Entry    Paper(PDF)

Automatic Derivation of Loop Bounds for WCET Analysis using Model Checking . Bachelor Thesis, TU Hamburg, December 2021 BibTex Entry   Paper(PDF)

User-Guided Random Testing using Input and Output Constraints. Master Thesis, TU Hamburg, November 2021 BibTex Entry   Paper(PDF)

Generating Controller Timed Automata for Nested Uppaal Strategies . Project Thesis, TU Hamburg, October 2021 BibTex Entry   Paper(PDF)

Low-Connectivity State Space Exploration using Swarm Model Checking on the GPU. Bachelor Thesis, TU Hamburg, September 2021 BibTex Entry   Paper(PDF)

Implementation of the flag removal algorithm and evaluation for coverage and computation time. Bachelor Thesis, TU Hamburg, September 2021 BibTex Entry   Paper(PDF)

Analysis and implementation of automatied data cleaning techniques for time series. Bachelor Thesis, TU Hamburg, September 2021 BibTex Entry   Paper(PDF)

An evolutionary approach for recovering UPPAAL timed automata from test traces. Bachelor Thesis, TU Hamburg, August 2021 BibTex Entry   Paper(PDF)

Recognizing dark patterns in online offers. Bachelor Thesis, TU Hamburg, August 2021 BibTex Entry   Paper(PDF)

Implementation and Experiments of Attacks on Social Networks. Bachelor Thesis, TU Hamburg, August 2021 BibTex Entry   Paper(PDF)

Implementation and evaluation of a feeling-based approach for privacy preservation in location-based services. Master Thesis, TU Hamburg, July 2021 BibTex Entry   Paper(PDF)

Translating interrupt-dependent assembler code for bounded model checking in SAL. Bachelor Thesis, TU Hamburg, July 2021 BibTex Entry   Paper(PDF)

A Low-Level Liquid Type System for Memory Safety. Bachelor Thesis, TU Hamburg, July 2021 BibTex Entry   Paper(PDF)

Compact Representation of Strategies for Hybrid Games. Master Thesis, TU Hamburg, May 2021 BibTex Entry   Paper(PDF)

Adaptation and implementation of a probabilistic process calculus for evaluating location privacy preserving algorithms. Master Thesis, TU Hamburg, March 2021 BibTex Entry     Paper(PDF)

Fault Localization in Labelled Transition Systems with Test Execution Analysis. Bachelor Thesis, TU Hamburg, February 2021 BibTex Entry     Paper(PDF)

Implementation of a scalable SMT Model Counting Solver. Bachelor Thesis, TU Hamburg, February 2021 BibTex Entry     Paper(PDF)

Transforming Uppaal Strategies into Controller Timed Automat. Bachelor Thesis, TU Hamburg, February 2021 BibTex Entry     Paper(PDF)

TypeChecking of Formulas in Spreadsheet Programs. Bachelor Thesis, TU Hamburg, January 2021 BibTex Entry     Paper(PDF)

Implementation and Evaluation of Learning Timed Automata from Interaction Traces Algorithm. Projektarbeit, TU Hamburg, November 2020 Bibtex entry   Paper(PDF)

Generation of Random Timed Automata for Uppaal . Projektarbeit, TU Hamburg, September 2020 Bibtex entry   Paper(PDF)

Automatic Simulation of Rare Events in UPPAAL using Timed Automata. Projektarbeit, TU Hamburg, August 2020 Bibtex entry    Paper (PDF)

Learning Probabilistic Automata For Rare Events. Bachelor Thesis, TU Hamburg, August 2020 Bibtex entry   Paper (PDF)

Improvements of Timed Automata through customizable Linting. Bachelor Thesis, TU Hamburg, August 2020 Bibtex entry   Paper (PDF)

Implementation and Simulation of an LLB-Algorithm for privacy Preserving location-based Services. Projektarbeit, TU Hamburg, July 2020 Bibtex entry   Paper (PDF)

Anomaly Recognition in Multivariate Time Series. Bachelor Thesis, TU Hamburg, July 2020 Bibtex entry   Paper (PDF)

Implementation and Evaluation of spatial generalization Algorithms for privacy Preservation in location-based Services. Projektarbeit, TU Hamburg, May 2020 Bibtex entry   Paper (PDF)

Quantitative runtime verification with unobservable state transitions. Master Thesis, TU Hamburg, March 2020 Bibtex entry   Paper (PDF)

Expressing Temporal Properties via Reachability Checks and Automata Extensions for ETAs. Projektarbeit, TU Hamburg, February 2020 Bibtex entry   Paper (PDF)

Retracing State Machine Evolution with respect to Preceding Specification using Reflection Models. Bachelor Thesis, TU Hamburg, January 2020 Bibtex entry   Paper (PDF)

Predicting Vessel Trajectories - A Comparison between RNNs and Kalman Filter . Master Thesis, TU Hamburg, December 2019 Bibtex entry   Paper(PDF)

Inference Attacks on Location Data and its Countermeasures. Bachelor Thesis, TU Hamburg, December 2019 Bibtex entry   Paper(PDF)

Constraint Logic Programming with Relaxation in Timetabling. Bachelor Thesis, TU Hamburg, December 2019. Bibtex entry

Optimizing Run-Time Performance of Look Ahead Online Model-Based Testing by Distributed Execution. Master thesis, TU Hamburg, January 2019. Bibtex entry   Paper (PDF)

Improving The Quality of Requirement Specifications Using Automatic Ambiguity Detection Tools . Master thesis, TU Hamburg, April 2019. Bibtex entry   Paper (PDF)

Incremental Measurement of Model Similarities in Probabilistic Timed Automata Learning . Master thesis, TU Hamburg, June 2019. Bibtex entry   Paper (PDF)

Automatic generation of fraudulent user behaviour . Master thesis, TU Hamburg, July 2019. Bibtex entry   Paper (PDF)

Lazy Synthesis of AIGER Circuits by solving Safety Games with NuSMV . Bachelor thesis, TU Hamburg, September 2019. Bibtex entry   Paper (PDF)

Optimizing CPU Inference Speed of Deep Neural Networks for Regression Tasks used in Real-Time Audio Applications . Master thesis, TU Hamburg, October 2019. Bibtex entry   Paper (PDF)

Lock Scheduling with Estimated Times of Arrival Based on Historical AIS Data . Master thesis, TU Hamburg, January 2019. Bibtex entry   Paper (PDF)

A Study of Generated versus Recorded Geolocation Data . Projektarbeit, TU Hamburg, February 2019. Bibtex entry   Paper (PDF)

An Analytic Time Metric for Execution Time Evaluation in Online Model Checking . Projektarbeit, TU Hamburg, March 2019. Bibtex entry   Paper (PDF)

Solving Symbolic Energy Games with NuSMV . Bachelor thesis, TU Hamburg, March 2019. Bibtex entry   Paper (PDF)

Formally Describing and Proving Privacy in Software Architectures. Master thesis, TU Hamburg, January 2018. Bibtex entry   Paper (PDF)

Structure and parametrization learning for probabilistic timed automata . Projektarbeit, TU Hamburg, June 2018. Bibtex entry   Paper (PDF)

Understanding and Improving Neural Network Classification . Projektarbeit, TU Hamburg, November 2018. Bibtex entry   Paper (PDF)

An Implementation of T-Closeness Through Steered Miroaggregation . Bachelor thesis, TU Hamburg, August 2018. Bibtex entry   Paper (PDF)

Computational Verification of Differentially Private Algorithms and an Improved Accuracy Estimate for the Sparse Vector Technique . Bachelor thesis, TU Hamburg, February 2018. Bibtex entry   Paper (PDF)

Transparent Quality Prediction for Software Requirements using Artificial Neural Networks . Master thesis, TU Hamburg, November 2018. Bibtex entry   Paper (PDF)

A New User Simulation-Based Approach for Application Performance Monitoring . Bachelor thesis, TU Hamburg, June 2018. Bibtex entry   Paper (PDF)

A layered architecture for time-series problems in logistics . Projektarbeit, TU Hamburg, 2018. Bibtex entry   Paper (PDF)

Trajectory Anonymisation by Multivariate Microaggregation . Bachelor thesis, TU Hamburg, July 2018. Bibtex entry   Paper (PDF)

Konsistenzprüfung von UML-Sequenzdiagrammen mit LTL-Spezifikationen in NuSMV . Bachelor thesis, TU Hamburg, July 2018. Bibtex entry   Paper (PDF)

Visual Analysis of Memory Usage for DSP-based Multi-core Systems . Master thesis, TU Hamburg, April 2018. Bibtex entry   Paper (PDF)

An Energy Type System for OMNeT++ Modules and their Extension by Energy Statistics . Bachelor thesis, TU Hamburg, November 2018. Bibtex entry   Paper (PDF)

A Type System for Energy-Aware-Progamming for Proceduaral Languages . Bachelor thesis, TU Hamburg, September 2018. Bibtex entry   Paper (PDF)

Winnowing Algorithm for Program Code. Bachelor thesis, TU Hamburg, July 2017. Bibtex entry   Paper (PDF)

Multric: Multiple Ranking Metrics Combined for Fault Localization . Projektarbeit, TU Hamburg, October 2017. Bibtex entry   Paper (PDF)

Ein Algorithmus für einen universellen Klassifikator zur Software Prediction . Bachelor thesis, TU Hamburg, March 2017. Bibtex entry

Goal-oriented Test-case Generation for iOS Mobile Applications . Master thesis, TU Hamburg, October 2017. Bibtex entry   Paper (PDF)

Automated Power Consumption Automata Analysis . Projektarbeit, TU Hamburg, February 2017. Bibtex entry   Paper (PDF)

Using Uppaal-SMC for Stochastic Modeling of Wireless Sensor Networks . Master thesis, TU Hamburg, September 2017. Bibtex entry

An Underapproximating Reachability Analysis for Hyrid Automata . Master thesis, TU Hamburg, April 2017. Bibtex entry   Paper (PDF)

Solving Floating-Point SAT by Optimizing a Represeting Function . Projektarbeit, TU Hamburg, July 2017. Bibtex entry   Paper (PDF)

Automated Verification of Song's Protocol for Smart Cards using ProVerif . Projektarbeit, TU Hamburg, July 2017. Bibtex entry   Paper (PDF)

Multiplication of BDD-Based Integer Sets for Abstract Interpretation of Executables . Bachelor thesis, TU Hamburg, March 2017. Bibtex entry   Paper (PDF)

Swarm Testing using CSmith . Projektarbeit, TU Hamburg, February 2017. Bibtex entry   Paper (PDF)

Feature Omission with C-Bounded Model Checking . Projektarbeit, TU Hamburg, February 2017. Bibtex entry   Paper (PDF)

LTL model repair . Master thesis, TU Hamburg, November 2017. Bibtex entry   Paper (PDF)

Implementation of Continuous Data Testing and an Application to a pharmaceutical Software . Bachelor thesis, TU Hamburg, June 2017. Bibtex entry   Paper (PDF)

Implementing a Type System on Differential Privacy for Security Protocols . Bachelor thesis, TU Hamburg, August 2017. Bibtex entry   Paper (PDF)

From UPPAAL to C: Code Generation and Semantic Model Constraints . Bachelor thesis, TU Hamburg, July 2017. Bibtex entry   Paper (PDF)

Mutation Analysis: A Case Study in Python. Master thesis, TU Hamburg, April 2016. Bibtex entry   Paper (PDF)

Code Generation for UML Activity Diagrams in Real-Time Systems . Master thesis, TU Hamburg, October 2016. Bibtex entry   Paper (PDF)

Semantic-Driven Model Repair for BDD-based Model Checking in NuSMV . Master thesis, TU Hamburg, July 2016. Bibtex entry   Paper (PDF)

Extracting and Using Power Channel Data for Automaton Inverence Using Sparse Alphabets . Bachelor thesis, TU Hamburg, June 2016. Bibtex entry   Paper (PDF)

GUI Ripping of iOS Mobile Applications . Project work, TU Hamburg-Harburg, April 2016. Bibtex entry   Paper (PDF)

Translation of Modelica Code into Hybrid Automata . Project work, TU Hamburg-Harburg, December 2016. Bibtex entry   Paper (PDF)

Automatic Code Synthesis of UML/SysML State Machines for Applications . Bachelor thesis, TU Hamburg, August 2016. Bibtex entry   Paper (PDF)

Automated C-Code Generation for SysML-based Architectures in the Context of Avionic Software . Bachelor thesis, TU Hamburg, August 2016. Bibtex entry   Paper (PDF)

Valid Initial States for the Reachability Analysis of Differential-Algebraic Equations . Projektarbeit, TU Hamburg, August 2016. Bibtex entry   Paper (PDF)

Combining Statistical and Online Model Checking based on UPPAAL and PRISM . Master thesis, TU Hamburg, October 2016. Bibtex entry   Paper (PDF)

Online Model Checking with UPPAAL SMC . Projektarbeit, TU Hamburg, February 2016. Bibtex entry   Paper (PDF)

Change- and Precision-sensitive Widening for BDD-based Integer Sets . Bachelor thesis, TU Hamburg, October 2016. Bibtex entry   Paper (PDF)

A hybrid architecture for pathfinding in dynamic environments . Master thesis, TU Hamburg, November 2016. Bibtex entry   Paper (PDF)

A Signedness-Agnostic Interval Domain with Congruences and an Implementation for Jakstab . Bachelor thesis, TU Hamburg, May 2016. Bibtex entry   Paper (PDF)

A Line Based Approach for Bugspots . Bachelor thesis, TU Hamburg, October 2016. Bibtex entry   Paper (PDF)

Automated Analysis of Natural Language Requirements using Boilerplates . Bachelor thesis, TU Hamburg, January 2016. Bibtex entry   Paper (PDF)

CTL Model Repair with NuSMV. Projektarbeit, TU Hamburg-Harburg, October 2015. Bibtex entry   Paper (PDF)

Increasing Accuracy at Generating Verified Sandboxes for Cyber-Physical Systems Using Reachability Computations . Master thesis, TU Hamburg-Harburg, February 2015. Bibtex entry   Paper (PDF)

Evaluating Angluin's Algorithm for Learning Integrated Circuits . Projektarbeit, TU Hamburg-Harburg, January 2015. Bibtex entry   Paper (PDF)

Teaching Angluin to Learn the Inner Workings of Integrated Circuits . Master thesis, TU Hamburg-Harburg, August 2015. Bibtex entry   Paper (PDF)

Entwurf und Entwicklung eines Metamodells und graphischen Editors für Hybride Automaten . Bachelor thesis, TU Hamburg-Harburg, March 2015. Bibtex entry   Paper (PDF)

Matching of Control- and Data-Flow Constructs in Disassembled Code . Bachelor thesis, TU Hamburg-Harburg, September 2015. Bibtex entry   Paper (PDF)

An Interval-Based Abstract Domain for Jakstab Supporting up to k Arbitrary Disjunctions . Bachelor thesis, TU Hamburg-Harburg, October 2015. Bibtex entry   Paper (PDF)

Applying Precision-Improving Rules to Reachability Computations for Hybrid Automata in HyCreate . Projektarbeit, TU Hamburg-Harburg, September 2015. Bibtex entry   Paper (PDF)

Improvement and Evaluation of Jakstab's Interval Domain with a Focus on Bitwise Operations . Bachelor thesis, TU Hamburg-Harburg, September 2015. Bibtex entry   Paper (PDF)

Semantic Preserving Transformations from ATLAS Testcase Descriptions to Teststand Sequences . Master thesis, TU Hamburg-Harburg, June 2015. Bibtex entry   Paper (PDF)

Fallbasiertes Schließen in strategischen Spielentscheidungen auf der NAO Robotik Plattform . Bachelor thesis, TU Hamburg-Harburg, August 2015. Bibtex entry   Paper (PDF)

Reimplementing Lintent: A Type Checker for Android Permission Configurations . Projektarbeit, TU Hamburg-Harburg, July 2015. Bibtex entry   Paper (PDF)

Transformation of GRAFCET-Based Control Specifications Into an IEC 61131-3 Implementation . Master thesis, TU Hamburg-Harburg, July 2015. Bibtex entry   Paper (PDF)

Investigating the Influence of Different Counting Conditions in Count Matrix Based Code Clone Detection . Bachelor thesis, TU Hamburg-Harburg, August 2015. Bibtex entry   Paper (PDF)

Static Data Flow Analysis for Vulnerable Permission Configurations of Android Applications . Projektarbeit, TU Hamburg-Harburg, January 2015. Bibtex entry   Paper (PDF)

Repository Mining at Machine-Code Level . Master thesis, TU Hamburg-Harburg, June 2015. Bibtex entry   Paper (PDF)

Flow-Insensitive Points-To Analyses for Frama-C Based on Tarjan's Disjoint-Sets. Bachelor thesis, TU Hamburg-Harburg, March 2014. Bibtex entry   Paper (PDF)

Fast SAT-Count for Labelless Complementable BDDs in BDDStab . Bachelor thesis, TU Hamburg-Harburg, July 2014. Bibtex entry   Paper (PDF)

Numerische Fehlerkorrektur und Programmflusskontrolle zur Behandlung von Soft Errors . Corrigendum, TU Hamburg-Harburg, August 2014. Bibtex entry   Paper (PDF)

Numerische Fehlerkorrektur und Programmflusskontrolle zur Behandlung von Soft Errors . Bachelor thesis, TU Hamburg-Harburg, July 2014. Bibtex entry   Paper (PDF)

Bidirectional Predicate Propagation in Frama-C and its Application to Warning Removal . Master thesis, TU Hamburg-Harburg, September 2014. Bibtex entry   Paper (PDF)

A Frama-C Plug-In for Finding Equal-Valued Expressions Using Dataflow Analysis . Projektarbeit, TU Hamburg-Harburg, January 2014. Bibtex entry   Paper (PDF)

Validierung des Schleifenerkennungsalgorithmus von Wei, Mao, Zou und Chen . Bachelorarbeit, TU Hamburg-Harburg, September 2014. Bibtex entry   Paper (PDF)

Entwicklung eines universellen Visualisierungs- und Datenverwaltungstools für asynchrone Messwerte . Bachelorarbeit, TU Hamburg-Harburg, October 2014. Bibtex entry   Paper (PDF)

Automated Generation of Unit Tests from UML Activity Diagrams using the AMPL Interface for Constraint Solvers . Master thesis, TU Hamburg-Harburg, January 2014. Bibtex entry   Paper (PDF)

Burrows-Wheeler compression with modified sort orders and exceptions to the MTF phase, and their impact on the compression rate . Bachelor thesis, TU Hamburg-Harburg, September 2014. Bibtex entry   Paper (PDF)

Test-Case Coverage Analysis of State-Based Automata for Fuel-Cell Systems in Commercial Aircraft . Bachelor thesis, TU Hamburg-Harburg, July 2014. Bibtex entry   Paper (PDF)

A GUI for Real-time Visualization of On-line Model Checking with UPPAAL . Bachelor thesis, TU Hamburg-Harburg, July 2014. Bibtex entry   Paper (PDF)

3D Modelling, Feature Extraction and Segmentation of Modern Warehouse Environments using Laserscanners . Master thesis, TU Hamburg-Harburg, September 2014. Bibtex entry   Paper (PDF)

Intraprocedural Control Flow Visualization based on Regular Expressions . Bachelor thesis, TU Hamburg-Harburg, January 2014. Bibtex entry   Paper (PDF)

Selecting Attributes for Automated Clustering of Disassembled Code from Embedded Systems . Bachelor thesis, TU Hamburg-Harburg, February 2014. Bibtex entry   Paper (PDF)

Clone Detection For Reserve Engineering of Disassembled Code . Bachelor thesis, TU Hamburg-Harburg, July 2014. Bibtex entry   Paper (PDF)

Grammar Transformations for Comparing Modelica Versions. Bachelor thesis, TU Hamburg-Harburg, July 2013. Bibtex entry   Paper (PDF)

Bitfehlerinjektionen in Register auf der Basis von FITIn . Bachelor thesis, TU Hamburg-Harburg, June 2013. Bibtex entry   Paper (PDF)

Anwendung von ASP auf Planungsprobleme . Studienarbeit, TU Hamburg-Harburg, November 2013. Bibtex entry   Paper (PDF)

Reusable QVT patterns for state machine model transformations and their verification in VMTS . Project work, TU Hamburg-Harburg, September 2013. Bibtex entry   Paper (PDF)

Model Checking of a Closed-Loop Medical Device System in UPPAAL . Projektarbeit, TU Hamburg-Harburg, February 2013. Bibtex entry   Paper (PDF)

Online Checking of a Hybrid Laser Tracheotomy Model in UPPAAL-SMC . Master thesis, TU Hamburg-Harburg, December 2013. Bibtex entry   Paper (PDF)

Automatic Case Analysis for Improving Path Sensitivity in Frama-C . Diplomarbeit, TU Hamburg-Harburg, March 2013. Bibtex entry   Paper (PDF)

Static Single-Assignment for Program Slicing on Binary Intermediate Language . Bachelor thesis, TU Hamburg-Harburg, May 2013. Bibtex entry   Paper (PDF)

Automatic Evolutionary GUI Testing Assisted by Static Analysis . Master thesis, TU Hamburg-Harburg, October 2013. Bibtex entry   Paper (PDF)

A Valgrind-based Soft Error Injection Tool for SIHFT Evaluations . Master thesis, TU Hamburg-Harburg, March 2013. Bibtex entry   Paper (PDF)

Evaluation of Standard Information retrieval system related to specific queries . Projektarbeit, TU Hamburg-Harburg, May 2013. Bibtex entry   Paper (PDF)

Modelling and Verification of QoS properties of a Biomedical Wireless Sensor Network. Projektarbeit, TU Hamburg-Harburg, February 2012. Bibtex entry   Paper (PDF)

Implementing a Multi-Target .NetCompiler for TouchDevelop . Diplomarbeit, TU Hamburg-Harburg, May 2012. Bibtex entry   Paper (PDF)

Detection of Zeno Sets in Hybrid Systems to Validate Modelica Simulations . Bachelor thesis, TU Hamburg-Harburg, July 2012. Bibtex entry   Paper (PDF)

Implementing Exhaustive Search for the Coil-in-the-box Problem using MPI . Project work, TU Hamburg-Harburg, June 2012. Bibtex entry   Paper (PDF)

Boolean Analysis for Path-senvitive Interprocedural Analyses of Asynchronous Programs . Bachelor thesis, TU Hamburg-Harburg, September 2012. Bibtex entry   Paper (PDF)

An interprocedural Points - To Analysis for Event-Driven Programs . Diplomarbeit, TU Hamburg-Harburg, January 2012. Bibtex entry   Paper (PDF)

Reverse Engineering und Analyse eines auf RS-232 basierenden Protokolls zur Reimplementierung . Diplomarbeit, TU Hamburg-Harburg, July 2012. Bibtex entry   Paper (PDF)

Das risikoorientierte und automatisierte Testen einer medizinischen Software mit Hilfe von Black-Box-Regressionstests . Diplomarbeit, TU Hamburg-Harburg, December 2012. Bibtex entry   Paper (PDF)

User Event Tracking for Test Case Generation for Web Applications . Projektarbeit, TU Hamburg-Harburg, February 2012. Bibtex entry   Paper (PDF)

Enhancing UPPAAL's Explanatory Power using Static Zeno Run Analysis . Diplomarbeit, TU Hamburg-Harburg, April 2012. Bibtex entry   Paper (PDF)

Scalability Analysis of the Simulin Design Verifier on an Avionic System . Bachelor thesis, TU Hamburg-Harburg, August 2012. Bibtex entry   Paper (PDF)

Investitgation of Sensor Networks using Algebraic Topology . Bachelor thesis, TU Hamburg-Harburg, November 2012. Bibtex entry   Paper (PDF)

Run-Time Load Analysis of Multi-Threaded Aplications by Inspection of Inter-Process Communication . Bachelor thesis, TU Hamburg-Harburg, July 2012. Bibtex entry   Paper (PDF)

Binary Analysis for Code Reconstruction of Control Software . Diplomarbeit, TU Hamburg-Harburg, October 2012. Bibtex entry   Paper (PDF)

A Data Partitioning Algorithm for Sound Particle Radiosity . Bachelor thesis, TU Hamburg-Harburg, August 2012. Bibtex entry   Paper (PDF)

Inferring Alias Contracts in VCC using Separation Analysis. Studienarbeit, TU Hamburg-Harburg, May 2011. Bibtex entry   Paper (PDF)

Symbolic Execution of nesC Programs . Studienarbeit, TU Hamburg-Harburg, April 2011. Bibtex entry   Paper (PDF)

An OBDD-based Representation of Sets of Integers for Frama-C . Studienarbeit, TU Hamburg-Harburg, April 2011. Bibtex entry   Paper (PDF)

Entwicklung eines Datenloggers für Beatmungsdaten und Mutationsanalyse für Robustheitstests . Bachelorarbeit, TU Hamburg-Harburg, August 2011. Bibtex entry   Paper (PDF)

Evaluation von Optimierungsalgorithmen zur Tourplanung im Hamburger Hafen . Diplomarbeit, TU Hamburg-Harburg, November 2011. Bibtex entry   Paper (PDF)

Entwurf einer komponentenbasierten und sicheren Formularbeschreibungssprache für heterogene Ausgabemedien . Diplomarbeit, TU Hamburg-Harburg, February 2011. Bibtex entry   Paper (PDF)

Performancetests für SQL Server 2008 bei der Verarbeitung räumlich-thematischer Daten . Bachelorarbeit, TU Hamburg-Harburg, November 2011. Bibtex entry   Paper (PDF)

Performance comparison of heuristic algorithms in routing optimization of sequencing traversing cars in a warehouse . Bachelor thesis, TU Hamburg-Harburg, April 2010. Bibtex entry   Paper (PDF)

Dynamic Invariant Detection for Sensor Network Applications . Projektarbeit, TU Hamburg-Harburg, December 2010. Bibtex entry   Paper (PDF)

Trafotest - A robust and fault-tolerant control software for transformer test stands . Bachelor thesis, TU Hamburg-Harburg, March 2010. Bibtex entry   Paper (PDF)

Evaluation von Optimierungsmaßnahmen bezüglich des Datencaches anhand von einfachen Algorithmen aus der Bildverarbeitung . Bachelor thesis, TU Hamburg-Harburg, July 2010. Bibtex entry   Paper (PDF)

Erkennung von Quellkodeduplikaten in WSN-Routingprotokollen mittels Klondetektoren . Bachelor thesis, TU Hamburg-Harburg, August 2010. Bibtex entry   Paper (PDF)

Inter-Context Control-Flow Graph for NesC, with Improved Split-Phase Handling . Studienarbeit, TU Hamburg-Harburg, July 2010. Bibtex entry   Paper (PDF)

Evaluation einer agentenbasierten Modellierung für ein AIS-Überwachungssystem mit optimierter Festmacheinsatzplanung. Diplomarbeit, TU Hamburg-Harburg, November 2009. Bibtex entry   Paper (PDF)

Conceptual-to-Oject Schema Mapping . Studienarbeit, TU Hamburg-Harburg, July 2009. Bibtex entry   Paper (PDF)

Program Transformation in Scala . Master thesis, TU Hamburg-Harburg, October 2009. Bibtex entry  

Language-Integrated Queries in Scala . Master thesis, TU Hamburg-Harburg, December 2009. Bibtex entry   Paper (PDF)

Master and Bachelor thesis

Writing a master or bachelor thesis is among the most important aspects of your studies. It is the opportunity to prove your successful studies and your academic abilities and to work together with experienced scientists from your university.

In general, if you are interested in the area of big geospatial data analysis and spatial data science, you might think about starting your thesis project with us. The interest of our chair becomes clear from the research page ( Forschung , Research ). As soon as you think your interests align with us, feel free to contact us for a meeting.

Here are a few examples of topics we are offering, however, just get in touch and we will find a good topic at the intersection of your interest and our research.

Example Topics

Alternative path topology for point cloud analysis.

Measured 3D point clouds are one of the most important data sources for autonomous driving, smart cities, and smart manufacturing. However, this data type provides its own challenges. In this thesis, you will work on extracting meaningful topological entities like windows in facades from analyzing families of alternative paths in a graph constructed by joining nearby points in 3D space.

Spatial Quantum Computing

Quantum computing is currently one of the interesting aspects of future computing architectures in that it provides significant speedup to certain clasically hard algorithms. We offer a few theses in which you construct your own quantum algorithms to solve selected spatial problems. We can offer access to a real quantum computer (IBM Q) through collaboration with our partners from the CODE research center at UniBW.

Bot Rejection from Precise Spatial Knowledge

Social media comprises one of the hot topics in big gespatial data analysis. But it is very hard to use as it is very noisy and in parts actively influenced by campaigns and bots. In this thesis project, you will extend an existing bot rejection scheme based on spatial computing and investigate the effect of bot and noise removal on a selected set of machine learning tasks involving social media data.

Indoor Mapping made cheap

Indoor Navigation and more generally acquiring spatial data in buildings is a challenging yet very valuable task. In this engineering thesis, you will build a simple indoor mapping system based on modern sensors and algorithms and analyze the impact of data fusion from cheap and expensive sensors.

Social Media Search and Retriveal

Internet search engines are widely used and accepted for organizing the knowledge available from the world wide web. In this thesis, you will extend our existing social media search engine with a set of aspects related to spatial influence and toponym mining. In this way, users are enabled to use keyword search together with spatial search in a flexible joint framework. This work is related to spatial analysis, spatial autocorrelation, information retrieval and machine learning.

Helena Fusion

Helena fusion is a project in which we design a novel high-level programming langauge enabling the automation of spatial machine learning flows and cross-sensor data fusion. Based on preliminary work in this area, we want to automate the process of spatial information mining and integrate a layer of data acquisition and coregistration. In this thesis, you contribute to this prestigious work and help us exploit cluster computing architectures for remote sensing image analysis in a systematic way.

Compression for Climate Land Cover Classification (ESA CCI)

Land cover classification is one of the more traditional areas of remote sensing. One of the application domains is climate change research. The ESA provides climate-relevant land cover data from an exceptionally interesting project https://www.esa-landcover-cci.org/ . Unfortunately, the data footprint of such global landcover mapping projects is huge and so is the amount of energy wasted in communicating and analyzing such data. In this thesis, we will discuss methods for early compression of data in projects related to climate-related land cover classification.

Martin Werner --> Professur für Big Geospatial Data Management Lise-Meitner-Str. 9 85521 Ottobrunn [email protected] Getting to us...

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• Chair of Computational Modeling and Simulation
• TUM School of Engineering and Design
• Technical University of Munich

• Bhattacharyya, I.: Assessment and Mitigation of Flooding Scenarios for the LMU Hospital, Munich through BIM-GIS Integration. Master thesis, 2024 more…
• Bulla, A.: A Bottom-Up Approach for the Automatic Creation of the Digital Staircase Model Using Point Cloud Data and Parametric Prototype Models. Master thesis, 2024 more…
• Harder, B.: A formal approach for algorithmic design of modular precast structures. Master thesis, 2024 more…
• Kirn, Hannes: Reconstruction of truncated instance point clouds with the help of generative models. Master thesis, 2024 more…
• Singh, Binod: Graph-based Entity Alignment: Adapting SGAligner for Point Cloud to BIM Alignment. Master thesis, 2024 more…
• Xu, Yingzi: Enhancing Prefabricated Building Design with BIM-based Modularization and Automated Transformation: A Case Study on Frame-Tube Structures. Master thesis, 2024 more…
• Berwal, A.: BIM based Autonomous Navigation of a Quadruped Robot. , 2023 more…
• Dao, V. C.: Parametric Building Energy Performance Simulation with Sensitivity Analysis Using BIM Models in Early Design Stages. Master thesis, 2023 more…
• Elshani, G.: Connecting Future Predictions of Railway Assets to BIM. , 2023 more…
• Espinosa, S.: Integration of laser profiler feedback into FIM-based additive manufacturing in construction. , 2023 more…
• Faßbender, C.: Time Integration for the Spectral Cell Method with Application to the Full Waveform Inversion. , 2023 more…
• Huber, S.: Entwicklung nichtplanarer Pfadplanungsmethoden zum Ausgleich von Materialverformungen eines 3D-gedruckten Bauteils. , 2023 more…
• Ibrahim, A.: Parameter-Based Model Reconstruction from Spacewise Segmented Point Cloud. Master thesis, 2023 more…
• Ibrahim, A.: Toward BIM-based ESG Assessment. , 2023 more…
• Kraus, S.: Möglichkeiten der modellbasierten Konformitätsprüfung von Brückenentwürfen des Straßenbaus mit IFC4x3. , 2023 more…
• Li, V.M.: Accelerating Topology Optimization Using a Combination of Conventional Methods and Neural Networks. , 2023 more…
• Nakrani, P.: Conceptual Framework of Construction Data Storage using Gaia-X Federation Services: Demonstration with Usecase of Project iECO. , 2023 more…
• Saleh, M.: Development of Quality Requirements for BIM-based facility management. Master thesis, 2023 more…
• Schmid, T.: Deep Learning-Based Surrogate Models for Linear Elasticity. , 2023 more…
• Ahmed, D.: Automatic Detection of Elements in the Technical Drawings of Bridges by Deep Learning and Parametric Modeling. , 2022 more…
• Aninger, A.: From Fabrication Information Models to Simulation Models. , 2022 more…
• Balota, B.: Change Selection and Feedback Communication of Design Variant Decision Using BIM. , 2022 more…
• Gaafar, R.: Checking mvdXML using mvdXML. , 2022 more…
• Giehl, A.: Validierung von BIM-Modellen auf Basis graphenbasierter Repräsentationen. Bachelor's thesis, 2022 more…
• Hofmeyer, J.: Evaluating environmental impacts of road routing alternatives using Building Information Modeling. , 2022 more…
• John, J.: Opportunities and current limitations of cloud-based design automation in the context of Building Information Modelling. , 2022 more…
• Kaya, S.: Analysis of the functionality of Building Information Modeling based Tendering-Awarding-Invoicing-Software. , 2022 more…
• Koleva, Betina: Model-based UAV mission planning for photogrammetric capture of existing buildings. Bachelor's thesis, 2022 more…
• Krishnakumar, H. K.: BIM-based optimization of the data acquiring process for construction and operational cost calculation. , 2022 more…
• Lang-Scharli, F.: Analysis of the current state and the use of digital twins in the operational phase of the infrastructure in the Free State of Bavaria including a comparison to the international state. , 2022 more…
• Mohamed, A.: Versioning of geometry representations in BIM models. , 2022 more…
• Ogunjinmi, G. J.: Estimating Circularity of Building Elements Using BIM. , 2022 more…
• Sattar, M. H.: Model Aware LiDAR Odometry and Mapping (MA-LOAM): Improving Simultaneous Localization and Mapping accuracy by robustly leveraging a Building Information Model. , 2022 more…
• Schnittger, HL.: Modellbasierte Unterstützung der Verkehrs- sicherheitsarbeit in der Straßenplanung. , 2022 more…
• Selimovic, E.: Sanierungspotential von Bestandsgebäuden mithilfe automatisierter geometrischer Rekonstruktion und semantischer Anreicherung aus Punktwolken. , 2022 more…
• Surendran Sanila, G.: An Object and History-based Approval method for MEP Slot and Opening planning in openBIM projects using a Database-driven workflow. , 2022 more…
• Taray, A.: Systematic Evaluation of the IFC Data Model for Infrastructural Assets and BIM Use Cases. , 2022 more…
• Tegeler, J.: IFC as data basis for noise immission simulations for transport facilities. , 2022 more…
• Weidinger, J.: Deep Learning based integration of manual changes on floor plans. , 2022 more…
• Zhang, S.: Model-based construction process simulation on a BIM example project. , 2022 more…
• Abdalaziz, A.: A More Reliable Method for Cost Estimation of Reinforcement Steel in Early Stages of Design. , 2021 more…
• Ali, S.: Automatic Classification and Consistency verification of Digital drawings using Deep Learning. , 2021 more…
• BASAK, A.: Image-Based Localization in 3D Point Clouds. , 2021 more…
• Dlubal, D.: Untersuchung des Structural Analysis Format (SAF) auf Eignung für eine BIM-gestützte Tragwerksplanung. , 2021 more…
• Du, C.: Enhancing 3D Point Cloud Semantic Segmentation Using Multi-Modal Fusion With 2D Images. , 2021 more…
• Federico Fernández Erbes: Parallel Phase-Field Simulations with the Finite Cell Method and Adaptive Refinement. Master thesis, 2021 more…
• Fischer, F.: Bewertungsmethodik zur modellbasierten Lebenszyklusbetrachtung der Technischen Gebäudeausrüstung in frühen Phasen anhand der Raumlufttechnischen Anlagen (KG 430). , 2021 more…
• Kannankattil Ajayakumar, J.: Classification of the Level of Geometry of Building Elements using Deep-learning. , 2021 more…
• Kelemen, Máté: A Review of Mass Lumping Schemes for the Spectral Cell Method. , 2021 more…
• Kiral, Alperen: Automated Calibration Methods for Digital Production Twins. , 2021 more…
• Kossat, R.: Point Cloud Completion by Deep Learning. , 2021 more…
• Lammers, B.: IFC-based variant analysis considering multicriterial sustainability analysis of buildings. , 2021 more…
• Liu, C.: Localizing and Matching CAD Model in Point Cloud Using Semantic Registration Method. , 2021 more…
• Müller, Bernhard: Business Innovation Framework for Industrialized Construction. , 2021 more…
• Pfitzner, F.: Data Mining within the as-performed construction process. , 2021 more…
• Samaras, D.: Automated Extraction of Semantic Information from Engineering Drawings using Deep Learning. , 2021 more…
• Schliski, S.: BIM-Based Code Compliance Checking of the Musterbauordnung. , 2021 more…
• Slepicka, M.: Fabrication Information Modelling - BIM-basierte Modellierung von Fertigungs-informationen für Additive Manufacturing. , 2021 more…
• Stocker, T.: Erstellung von IFC-Datenmodellen für den Holzbau und darauf basierende automatisierte Überprüfung der Einhaltung von Schallschutzanforderungen. , 2021 more…
• Xia, Y.: Automated Methods of Mapping LCA Data to BIM Models. , 2021 more…
• Bollig, YC.: Geometrical and Topological Linking of Railway Systems. , 2020 more…
• Collins, F.: Encoding of geometric shapes from Building Information Modeling (BIM) using graph neural networks. , 2020 more…
• Drewes, L.: BIM-integration of sustainable building certification criteria in the early design stages. , 2020 more…
• Jokeit, Moritz: Exploring Physics-informed Neural Networks for the Heat Equation. , 2020 more…
• Kolbeck, L.: Interoperability of BIM-based Life-Cycle Energy Analysis in Early Design Stages. , 2020 more…
• Liu, Chenyang: Modification of Parameters in image-based automated 3D-Reconstruction for Fine Structures. , 2020 more…
• Popgavrilova, G.: Assuring building information quality for building analytics by translating use cases of BIM@SRE standard into the MVD format. , 2020 more…
• Schlenger, J.: Integration getrackter Eisenbahnausrüstung in eine Fachmodellumgebung. , 2020 more…
• Siebenhütter, K.: Entwicklung einer Methode zum Festhalten des Standes von geprüften Bauwerksmodellen. , 2020 more…
• Speiser, K: Ein Ansatz zur Anreicherung und Validierung von IFC-Modellen durch das Übersetzen gegebener Daten in standardisierte Informationsanforderungen. , 2020 more…
• Stoitchkov, D.: Automated retrieval of shared IFC model data based on user-specific requirements. , 2020 more…
• Vega, M.: Efficient Vertical Object Detection in Large High-Quality Point Clouds of Construction Sites. , 2020 more…
• Ahmad, A.: Untersuchungen zur Modellierung und Projektabwicklung für den Erdbau einer freien Bahnstrecke. , 2019 more…
• Beck, F.: Categorization and visualization of model-based informational distance during the BIM-based design process Master. , 2019 more…
• Breden, Steve: Entwicklung einer Anwendungssoftware zur Unterstützung der Bauausführung. , 2019 more…
• Breu, T.: Point Cloud Classification as a Support for BIM-based Progress Tracking. , 2019 more…
• Georgoula, V.: Development of an Autodesk Revit Add-in for the Parametric Modeling of Bridge Abutments for BIM in Infrastructure. , 2019 more…
• Hacker, D.: Abbildung der zeitgebundenen Kosten im bergmännischen Tunnelvortrieb mit der BIM-Methodik. , 2019 more…
• Holland, Michael: Datenanalyse in der Cloud – Entwicklung eines Prototyps zur Automatisierung von Baudokumentation und Baufortschrittskontrolle mit den Prinzipien des Internet of Things. , 2019 more…
• Hölzlwimmer, V.: Prüfung von Fertigstellungsgraden in digitalen Gebäudemodellen. , 2019 more…
• Jäger, Michael: Anwendungen von BIM zum Projektmanagement mit Lean Construction. , 2019 more…
• Mair, L.: Levels of Development (LODs) bei der Erstellung von Auftraggeber-Informationsanforderungen (AIAs) für Straßenbauprojekte am Beispiel der „Grundhaften Erneuerung der A92“. , 2019 more…
• Meinberg, L.: Erarbeitung eines Softwarekonzepts zur Verbesserung der Kommunikation zwischen Projektbeteiligten auf der Baustelle während der Bauausführung. , 2019 more…
• Nguyen, T: Entwicklung eines Detaillierungskonzepts für die BIM-basierte Modellierung von Massivbaubrücken. , 2019 more…
• Rakic, M.: Lean BIM-based communication and workflow during design phases. , 2019 more…
• Rohrmann, J.: Design Optimization in Early Project Stages. , 2019 more…
• Sedlmair, M.: Point Cloud Processing for Tunnel Infrastructures. , 2019 more…
• Seelos, S.: A Framework for generating building models for graph-based neural networks. Technische Universität München, 2019, more…
• Stauch, F.: BIM im Spezialtiefbau – Einsatz wissensbasierter Methoden zur Verbesserung der Modellqualität und Steigerung der Modellierungsgeschwindigkeit. , 2019 more…
• Tadesse, R.: BIM im Tunnelbau - Entwicklung eines Informationsmodells für Tunnelbauwerke. , 2019 more…
• Verena, Wolf: Entwicklung eines Konzeptes zur Bewertung digitaler Datenmodelle am Beispiel einer Bahnsteigplanung. , 2019 more…
• Barth, A.: Development of an integrated data management in civil engineering with the help of methods of the Systems Engineering. Master thesis, 2018 more…
• Begana, K.: Uncertainties in BIM-based Life Cycle Assessments in early design phases. Master thesis, 2018 more…
• Esser, S.: Implementierung einer Datenschnittstelle zur Unterstützung der modellbasierten Planung von Bahnausrüstungstechnik. Master thesis, 2018 more…
• Fehrenbach, A.: Definition von Modellinhalten für BIM-Modelle von Schleusenbauwerken für ausgewählte BIM-Anwendungsfälle der Planung. Master thesis, 2018 more…
• Forth, K.: BIM-basierte Ökobilanzierung. , 2018 more…
• Hinterschwepfinger, J.: BIM-gestütztes Anforderungsmanagement zur Kalkulation eines Hochbauprojektes. Bachelor thesis, 2018 more…
• Kirn, F.: Building Information Modeling and Virtual Reality: Editing of IFC Elements in Virtual Reality. , 2018 more…
• Reichle, Johannes: Anwendungspotentiale von BIM im Bauprozessmanagement. Master thesis, 2018 more…
• Xu, S.: Investigation of graph-databases for storing and analyzing building models. Master thesis, 2018 more…
• Zibion, D.: Development of a BIM-enabled Software Tool for Facility Management using Interactive Floor Plans, Graph-based Data Management and Granular Information Retrieval. Master thesis, 2018 more…
• Bareth, Thomas: Baudimensionierung hinsichtlich Fluchtwegesicherheit und Komfort – eine Betrachtung von ingenieurtechnischen Berechnungsmethoden vor dem Hintergrund der baurechtlichen Vorschriften. , 2017 more…
• Beutelrock, M.: Bausystemschnittstellenentwicklung im industriellen Bauen. , 2017 more…
• Hudeczek, D.: Formalisierung von Normen mithilfe von Auszeichnungssprachen für die automatisierte Konformitätsüberprüfung. , 2017 more…
• Koebler, K.: Untersuchung der IFC-gestützten Modellübertragung zur Ableitung von Modellierempfehlungen für Architekten. , 2017 more…
• Kopp, Philipp: Multi-level hp-FEM and the Finite Cell Method for the Navier-Stokes equations using a Variational Multiscale Formulation. Master thesis, 2017 more…
• Lahr, S.: Durchführung einer mikroskopischen Personenstromanalyse zur Optimierung und Evaluation der Abläufe, von Umbaumaßnahmen des Münchner Hauptbahnhofes. , 2017 more…
• Lauterbach, Sven: Performanceoptimierung von Fußgängersimulationen durch Einsatz von Parallelisierungstechniken. , 2017 more…
• Schwab, Benedikt: Automated Driving: Analysis of Standard-Setting Dynamics and Development of a Pedestrian Simulation Model. Bachelor thesis, 2017 more…
• Sun, Jingxing: Untersuchung der BIM-basierten Arbeitsweise im Verkehrswegebau eingebettet in die Planungsphase. Bachelor thesis, 2017 more…
• Vega, S.: Analysis of BIM-based Collaboration Processes in the Facility Management. , 2017 more…
• Wang, Y.: Analysis of Code and Guideline Contents in Construction Industry based on Text Mining. Bachelor thesis, 2017 more…
• Cheng, Zhibin: Modelling Pedestrian Group Behaviors on a Music Festival Event Using an Agent-based Method. , 2016 more…
• Iqbal, M.: Advanced Topological Operators for QL4BIM. , 2016 more…
• Kunkel, H.: Digitales Bauen - Integration von projektorientierten Informationssystemen im schlüsselfertigen Hochbau. , 2016 more…
• Marx, M.: Computergestützter Optimierungsprozess zur Unterstützung der Entscheidungsfindung in der frühen Entwurfsphase am Beispiel eines nachhaltigen Museumsgebäudes. , 2016 more…
• Mini, F.: Entwicklung eines LoD Konzepts für digitale Bauwerksmodelle von Brücken und dessen Implementierung. , 2016 more…
• Prasad, R.;: Betrachtung und Analyse des Projektmanagements im BIM-gestützten Bauprozess. , 2016 more…
• Faure, Julien: Vergleich und Bewertung von Analysewerkzeugen für die Validierung und Kalibierung von mikroskopischen Personenstrommodellen. Master thesis, 2015 more…
• Schneider, Michael: Einführung der BIM-Methode im Ingenieurbüro – Unterstützung der Abläufe durch eine durchgängige Nutzung einer Bauteilbibliothek. Master thesis, 2015 more…
• Seeser, E.: Entwicklung eines Add Ins basierend auf Siemens NX zum Datenaustausch in die CADINP Sprache von SOFiSTiK. , 2015 more…
• Sojka, Frédéric: Integration des Building Information Modelling in den Wertschöpfungsprozess eines mittelständischen Bauunternehmens. , 2015 more…
• Büchele, D.: Visualisierung von Fußgängersimulationsdaten auf Basis einer 3D-Game-Engine. , 2014 more…
• Frank, J.: Realistische Echtzeit-Visualisierung von CFD-Ergebnissen. Master thesis, 2014 more…
• Hua, Shan: Entwicklung einer Schnittstelle zwischen IFC-Gebäudemodellen und Modelica. Master thesis, 2014 more…
• Hölderle, B.: Untersuchung von Autodesk Vault für den BIM-Prozess. , 2014 more…
• Kuloyants, V.: Entwicklung eines IFC-basierenden Datenaustauschstandards für den Unterbau von Brückenbauwerken. , 2014 more…
• Kutterer, B.: Computergestützte Tragwerksplanung im Holzbau. , 2014 more…
• Preidel, C.: Entwicklung einer Methode zur automatisierten Konformitätsüberprüfung auf Basis einer graphischen Sprache und Building Information Modeling. Master thesis, 2014 more…
• Singer, D.: Entwicklung eines Prototyps für den Einsatz von Knowledge-based Engineering in frühen Phasen des Brückenentwurfs. , 2014 more…
• Vilgertshofer, S.: Repräsentation und Detaillierung parametrischer Skizzen mit Hilfe von Graphersetzungssystemen. , 2014 more…
• Weinholzer, M.: Analyse und Implementierung von Datenaustauschformaten zwischen CAD- und AVA-Systemen im Zuge einer BIM-basierten Projektrealisierung im Ingenieurbau. , 2014 more…
• Braun, A.: Entwicklung eines 4D-BIM-Viewers mit graphbezogener Darstellung von Bauabläufen und - alternativen. , 2013 more…
• Hofmeier, M.: Entwicklung einer Software zur Soll/Ist-Bauablaufvisualisierung mit IFC-Schnittstelle. , 2013 more…
• Nasyrov, Vladislav: Building Information Models als Input für energetische Gebäudesimulation. Master thesis, 2013 more…
• Andrae, M.: Entwicklung eines Mangelaufnahme-Systems auf Mobilen Geräten für den Einsatz bei der Objektüberwachung zur weiteren zentralen Verarbeitung. Master thesis, 2012 more…
• Wang, M.: 3D-Planung von Brückenbauwerken mit Siemens NX 7.5. , 2012 more…
• Ritter, F.: Untersuchung der Möglichkeiten und Vorteile des modellgestützten kooperativen Planens anhand von Autodesk Produkten. Master thesis, 2011 more…
• Solis Lopez, J.M.: Calculation and representation of structural reinforcement in Building Information Models using Revit Structure and SOFiSTiK. , 2011 more…
• Zhang, Y.: Genetic Algorithms for Bridge Maintenance Scheduling. , 2010 more…
• Zhou, H.: Development of An Earthwork Simulation Model with Plant Simulation. Master thesis, 2010 more…
• Lu, Y.: Development of the 4D Earthwork ViZ Toolkit Applied in Road Construction. Master thesis, 2009 more…
• Ramos Jubierre, J.: Analysis and coupling of a Geometric Constraint Solver with a CAD application. , 2009 more…

AMD's $275 Technical Trajectory In 2024

• Advanced Micro Devices, Inc. is capitalizing on AI and tech markets, eyeing over $50 billion in chip demand by 2024, despite Nvidia Corporation's lead.
• Post-peak, AMD's stock corrects 25% with a pivot at $152, setting sights on a $275 target in 2024.
• China's tech restrictions threaten AMD's significant revenue amid growing US-China tech tensions.
• AMD's MI300X AI GPU and chiplet innovations, alongside key partnerships, highlight its push for market growth amidst challenges.
• Yiazou Capital Research members get exclusive access to our real-world portfolio. See all our investments here »

Jonathan Kitchen/DigitalVision via Getty Images

Investment Thesis

Advanced Micro Devices, Inc. ( NASDAQ: AMD ) has emerged as a breakout stock amid the AI frenzy owing to its massive business potential in data centers, gaming, and PCs. With demand for specialized chips optimized for generative AI expected to exceed $50 billion by 2024 , the company has a tremendous opportunity to capitalize on this.

The world's AI chip market should reach between $100 billion and $400 by 2024. This explains AMD's investment growth, which signals it aims to strengthen its prospects in a burgeoning sector. It also explains why the company has sought to strengthen its prospects in other growing sectors beyond generative AI chips, with the knowledge that AI chip sales account for only 11% of the global chip market.

AMD still has a lot more left in its tank, though it is more likely to play second fiddle to Nvidia ( NVDA ), which has emerged as the poster child of the AI revolution. Undoubtedly, Nvidia is still the market leader in offering chips designed specifically for training and running AI applications. This might explain why the stock has rallied by over 100% over the past six months, during which AMD has gained about 66%.

AMD's chipset-based design strategy contrasts sharply with Nvidia's monolithic approach, potentially reshaping the GPU market by offering cost-effective, scalable solutions. This innovation, coupled with strategic moves into AI and large language models, or LLMs, positions AMD to challenge Nvidia's dominance despite the latter's strong ecosystem and performance edge.

Pivoting at $152 with Eyes on $275 Target

AMD is in a downtrend, as after reaching a high of $227.30, the stock experienced over a 25% correction. Following the current momentum and key Fibonacci levels, the price may hit $152 in the coming days. $152 serves as the pivot for the current price swing, which may provide solid support for the price.

Looking at the Relative Strength Index (RSI), at 56, the stock price is in a neutral state. In an optimistic outcome, the price may take support at the pivot with the RSI near 50 and reverse its momentum to hit the primary resistance at $186 . In the worst-case scenario, the price may hit $118, vital long-term support. In such a case, RSI may reach near 30 on an oversold trajectory before making an upward recovery. However, this bearish scenario is less likely.

Notably, the $152 also aligns with the multiple norms for price-to-earnings valuation. The price-to-earnings ratio (on a trailing twelve-month basis) is near 63, while AMD’s long-term price-to-earnings average sits near 56. This signifies a 12% overvaluation. The price has to reach nearly $150.15 to attain a fair valuation. Based on that assumption, the pivot may divert the price into the bullish trajectory again.

Given the shift in the recent bullish trend and Fibonacci indicators, we're setting an ambitious 2024 price target of $275 for AMD and upgrading our rating to a strong buy .

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China's New Tech Guidelines Threaten AMD's Revenue

One catalyst behind AMD's pullback is reports that China plans to introduce new guidelines that will make it difficult for the company to sell its chips in the country. Plans to phase out the use of U.S. microprocessors in government PCs and servers are another headwind that has rattled investors' sentiments about the stock.

China is taking significant steps to build domestic substitutes for foreign technology. In addition, its ban of microprocessors from AMD, Intel ( INTC ), and others comes amid growing tensions with the U.S. over certain technologies that the countries bill as a matter of national security. Consequently, Beijing has already published a list of safe and reliable processors and operating systems from Chinese companies designed to undercut AMD, Intel, and Microsoft ( MSFT ).

AMD is one of the biggest suppliers of chips used in PCs and thus would be the most brutally hit should Beijing implement its plans. Last year alone, the company generated nearly 15% or about $3.5 billion of its $23 billion sales in China. Therefore, any ban would significantly impact one of the company's key revenue streams.

AMD's revenue breakdown

AMD vs. Nvidia: The Chiplet Revolution Upends the GPU Market

AMD's technology uses a modular chiplet design, combining small chips to make one large processor. This improves manufacturing yields and production costs since one defect does not render the whole chip unusable. Think of it like Lego: It is much easier to snap together small building blocks than carve a large statue from one piece of plastic.

On the other hand, although Nvidia's traditional monolithic chip design performs well, it raises prices and profits due to an all-or-nothing manufacturing process. The MI300 chip from AMD, incorporating the revolutionary chiplet technology in GPUs, is a strategic evolution that may eventually disturb Nvidia's market dominance.

While generally at lower margins, the competitiveness of chiplet designs emphasizes a reduction in cost and constant technological improvement of the lauded Radeon RX 6000 series, AMD has added a new facet to its high-margin business and will be on a potentially disruptive collision course with its rival, Nvidia's lofty standing. Over the long run, Nvidia's monolithic design and high-profit commitment may create a barrier to adapting to the chiplet approach, which might result in losing ever more market trends to AMD .

Nvidia does hold a strong position in the GPU market, much like Microsoft did in the past, and its CUDA platform has attracted a vast ecosystem of developers. However, AMD's focus on the AI inference market and introducing hardware with significant AI capabilities, like the MI300X optimized for large language models, presents a strategic shift.

Lastly, AMD's chips, offering up to 192GB of HBM3 memory, can handle larger models than Nvidia's A100 , showcasing AMD's potential to compete in specific niches of the AI market.

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AMD Unleashes MI300X AI GPU to Challenge Nvidia's Dominance

Over the years, CPUs for personal computers and servers have always been the main business of semiconductor companies. However, AI chips have been increasingly taking over thanks to the launch of new chips in recent quarters.

The company is increasingly investing to position itself in the AI market, which hit highs of $200 billion last year and is expected to expand to a compound annual growth rate of 37% by 2030 . The stakes are high, and the industry is expected to be worth $2 trillion by the decade's end.

While Nvidia controls close to 90% of the AI GPU market share, AMD is putting up a fair fight. The company is already looking to challenge the status quo by launching the MI300X AI GPU, designed to compete with Nvidia's H100 chipsets in the segment.

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The MI300X AI GPU stands out as cheap, thus lowering the costs of developing AI models for potential clients. While AMD has yet to reveal the pricing of the MI300X chip, it is not expected to cost more than $40,000 a chip, which is the cost of one chip of the Nvidia H100.

Additionally, it is based on a new architecture that leads to significant performance gains compared to the H100. Integrating a 192GB cutting-edge, high-performance memory dubbed HBM3 means the AI chipset can transfer data faster while fitting larger AI models. The CEO has already reiterated that the MI300 chipset will be the fastest to ramp up $1 billion in sales.

The biggest question has always been whether companies already using Nvidia AI GPUs would be ready to switch to another alternative or GPU supplier. AMD has addressed the issue by updating its software suite, dubbed ROCm, to better compete against Nvidia's industry-standard CUDA software. Thus, the update addresses why most developers had opted for the Nvidia chip.

Following the software update, AMD has already signed up Microsoft (MSFT) and Meta Platforms ( META ) as its new customers for the MI300X AI chip. The two were the largest purchasers of H100 GPUs, which helped cement Nvidia's control of the segment. OpenAI, spearheading the AI revolution, has already indicated that it will support AMD GPUs in its software products used for AI research.

Finally, while AMD is projecting nearly $3.5 billion in AI chip sales, there is still a massive untapped market opportunity, given that the market could climb to $400 billion over the next few years. Therefore, the high expectations explain why the company focuses entirely on the new product line.

AMD's New AI Processors Target PC Revival, Gaming Industry Lead

In addition to pursuing AI growth, AMD is fixated on improving the PC market in recovery mode following the COVID-19-triggered slowdown. While PC shipments dipped by 16% in 2022 amid skyrocketing inflation, they showed signs of improvement in 2023. The company has unveiled a new desktop processor called AI PCs for the high-end personal computers market.

Ryzen 8000G Series is AMD's new line of desktop PC processors, which it plans to use to strengthen its prospects in the sector and take on Intel. The new processors are designed for intensive gaming and content creation applications and optimized for local AI workloads.

The launch follows the unveiling of the 8040 Series processor late last year, which the company hopes to use to pursue market share on the notebook AI PCs. The new line of PC chips is already eliciting strong interest from PC makers, including Acer, Asus, Lenovo, and HP ( HPQ ), at a time when PC sales are on the rise.

Finally, AMD has also introduced the Radeon RX 7600 XT graphics cards that it plans to use to strengthen its prospects in the multibillion-dollar gaming industry. The chip is meant to demand PC games and create content.

Bottom Line

AMD is firing on all angles, even as it appears to play second fiddle to Nvidia in the AI chips market sector. Backed by a strong product line of chips targeting opportunities in the data center, personal computer, and gaming industry, the company is in a solid position to generate long-term value. Its net income grew by over 3000% in the fourth quarter, underlining its robust growth phase.

Likewise, AMD is well positioned to take on Nvidia and eat a significant market share, having unveiled the MI300X, a much more powerful and cost-effective AI GPU. Given the ever-growing demand for chips to power AI-generative services and hardware, there is plenty of room for growth, which positions AMD to offer investors bigger gains over the long term.

Author of Yiazou Capital Research

Unlock your investment potential through deep business analysis.

I am the founder of Yiazou Capital Research , a stock-market research platform designed to elevate your due diligence process through in-depth analysis of businesses.

I have previously worked for Deloitte and KPMG in external auditing, internal auditing, and consulting.

I am a Chartered Certified Accountant and an ACCA Global member, and I hold BSc and MSc degrees from leading UK business schools.

In addition to my research platform, I am also the founder of a private business.

This article was written by

Yiannis Zourmpanos is a Charter Certified Accountant, a former corporate auditing consultant and a Fellow Member of ACCA Global with both BSc and MSc degrees. He is also a private business owner.

Analyst’s Disclosure: I/we have a beneficial long position in the shares of AMD either through stock ownership, options, or other derivatives. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Seeking Alpha's Disclosure: Past performance is no guarantee of future results. No recommendation or advice is being given as to whether any investment is suitable for a particular investor. Any views or opinions expressed above may not reflect those of Seeking Alpha as a whole. Seeking Alpha is not a licensed securities dealer, broker or US investment adviser or investment bank. Our analysts are third party authors that include both professional investors and individual investors who may not be licensed or certified by any institute or regulatory body.

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