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Artificial Intelligence
Fakultät für Informatik
Skyline of TU Dortmund University with sunset in the background © Ekkehart Reinsch

Writing a Thesis at Our Group

Please do not hesitate to contact any member of our group if you are interested at writing a thesis at our group. 

Requirement:

  • To write a thesis with us, you should have successfully completed at least one of our courses (lecture or “Fachprojekt”)

Completed Theses 

2023-Present at Technical University of Dortmund

Bachelor Theses:
  • Exploring the Soft Actor-Critic Algorithm for Continuous Control
  • TARFlow
  • Rechenzentren als Virtuelle Batterie
  • Noise Reduction in Distributed Acoustic Sensing: From Filtering Techniques to Deep Learning Approaches
  • Learning Discrete Representations for Distributed Acoustic Sensing using Vector-Quantized Variational Autoencoders
  • Combining Monte-Carlo-Tree-Search, Deep Learning and Rule-Based Grading for Symbolic Music Generation
  • xLSTM: Insights into Memory and Gating Dynamics
  • Fiber Optic Cable Localization via Distributed Acoustic Sensing
  • Improving LLM initialization for heuristic SAT solvers using reinforcement learning
  • Validating the Effectiveness of Directional Noise in Diffusion-Based Graph Representation Learning
  • Symbolic Music Genre Transfer with Generative Models
  • Decentralized Federated Learning: Enhancing IFCA for Efficient Clustered Model Training
  • Image Captioning with Generative Image-to-text Transformers
  • Providing Bipartite GNN Explanations with PGExplainer
  • Recurrent Graph Neural Networks for SAT Problem Solving
  • Zero-Shot Denoising of Distributed Acoustic Sensing Data using Deep Priors
  • Das Lösen von Online 3D Bin Packing mit Deep Reinforcement Learning und Transformern
  • Prioritizing Samples in DQN: The Evolution from Random to Reducible Loss
  • Speech Enhancement and Audio Deconvolution with Deep Learning
  • Probabilistically modelling Self-Supervised Learning
  • Wavelet-based Clustering of DAS Data
  • AlphaZero Approach for Dice Games with Various Levels of Complexity
  • Classification of microanatomical structures of the mouse liver by machine learning from intravital multiphoton microscopy videos
  • An Empirical Analysis of Self-built GPT Models for GLUE Task Performance
  • Solving Inverse Problems using Score-based Diffusion Models
  • Phase Retrieval With Denoising Diffusion Probabilistic Models
Master Theses:
  • Exploring MCTS on Team Games with Imperfect Information
  • Structured World Models through Graph Neural Networks
  • Plasticity in Spiking Neural Networks
  • Causal Discovery with Missing Data
  • Seismic Arrival-time Picking on DAS Data with Deep Learning
  • Distributional Reinforcement Learning with Score Functions
  • Learning To Explore: A Comprehensive Study And Implementation Of Exploration-Based Reinforcement Learning
  • Entrauschen von DAS Daten mit Noise2X / Denoising of DAS Data Using Noise2X
  • Deep Learning-Based Cellular Feature Analysis in Digitized Tissue Samples
  • Deep reinforcement learning for SAT solver heuristics
  • Data Augmentation Methoden für Deep Learning
  • Visual Enhancement of Whole Brain Slide Images from Z-Scanning Microscopes with Deep Style Learning

2015-2022 at University of Düsseldorf

Bachelor Theses:
  • Active pre-training with phasic policy gradient
  • Classification models for argument recommender systems
  • Phase retrieval with attention
  • Deep learning of financial market dynamics
  • Multi-stage progressive image dehazing
  • Contrastive self-supervised pretraining of vison transformers
  • Erweiterung und Evaluation der Neural Power Unit
  • Pay attention to what you calculate: transformer-based approach on recognition of handwritten mathematical expressions
  • Ein nicht-operatorbasierter Ansatz für das Conditional Kernel Mean Embedding
  • Using unrolled networks for reference based Fourier phase retrieval
  • Echtzeiterkennung mit YOLO
  • Dynamically modifying ML programs for automated machine learning
  • Learning by self-play in turn-based environments
  • Natural gradient boosting for classification
  • Deep cascading Fourier phase retrieval
  • Post-hoc model interpretability vs. intrinsic model interpretability
  • Implementing and benchmarking various classes of normalizing flows
  • Graph-based semi-supervised leraning with GPs
  • Graph-based semi-supervised learning: the distribution of labels
  • Learning to write to learn to read
  • MRI contrast mapping using machine learning
  • Lottery ticket hypothesis - seeking capable subnetworks
  • Integrating the Game CATAN into the RL Framework OpenSpiel
  • Machine Learning auf Sätzen und ihren semantischen Frames
  • Konsistente Kernel Erwartungswert-Schätzung für Funktionen von Zufallsvariablen
  • Implementing Continuous High-Resolution Image Reconstruction using Patch Priors
  • Adversarial attacks on capsule networks
  • Implementing survey propagation
  • Optimization of submodular functions
  • Nicht-lineare ICA mit neuronalen Netzen
  • Chatbots with deep learning
  • Implementing AlphaZero for small board games
  • Playing Go with Recurrent Neural Networks
  • Actor-critic reinforcement learning with experience replay
  • Proximal policy optimization (PPO)
  • Reimplementing and extending Tesauro's TD-Gammon
  • A ML approach to detect and classify spores in microscopy images
  • Actor-Critic Reinforcement Learning
  • Analyzing Brain Images with Deep Learning
  • Deep Q-Learning in TensorFlow
  • Grade prediction with machine learning
  • Implementation of variational autoencoder
  • Classification of data from the ATLAS experiments
  • Collaborative filtering
  • Causal relations for two random variable
  • Representing distributions as mixture of Gaussians
  • Finding stars in traces      
Master Theses:
  • Why don't we have robust classifiers?
  • Could Brothers Grimm Create a Dictionary with BERT?
  • Topic-aware approaches to Natural Language Processing
  • Latent optimization for deep generative phase retrieval
  • Cyclophobic reinforcement learning
  • Erweiterung von fortschrittlichen Reinforcement Learning Algorithmen
  • Classical and integer linear programming approaches to learning causal structure
  • Towards better understanding stochastic gradient descent for deep learning
  • World models for reinforcement learning
  • Measuring the similarity of arguments with BERT
  • Scaling deep reinforcement learning
  • Deep learning methods for phase retrieval
  • Alpha matting revisited
  • Capsules for generative adversarial networks
  • Bayesian methods for deep learning
  • MCMC for Bayesian computation in causal inference
  • Multiframe blind deconvolution with lots of noise
  • Probabilistic programming in Julia