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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:

  • You should have successfully taken one of our classes (lecture/seminar) when you want to write a thesis with us.

Completed Theses 

2023-Present at Technical University of Dortmund

Bachelor Theses:
  • Phase Retrieval With Denoising Diffusion Probabilistic Models 
  • Solving Inverse Problems using Score-based Diffusion Models
  • An Empirical Analysis of Self-built GPT Models for GLUE Task Performance
  • 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 
  • UEFA EURO 2024 prediction
  • Wavelet-based Clustering of DAS Data
  • Probabilistically Modelling Self-Supervised Learning
  • Speech Enhancement and Audio Deconvolution with Deep Learning
  • Prioritizing Samples in DQN: The Evolution from Random to Reducible Loss
  • Recurrent Graph Neural Networks for SAT Problem Solving
  • Zero-Shot Denoising of Distributed Acoustic Sensing Data using Deep Priors
Master Theses:
  • Data Augmentation Methoden für Deep Learning 
  • Deep Reinforcement Learning for SAT Solver Heuristics
  • Deep Learning-Based Cellular Feature Analysis in Digitized Tissue Samples
  • Learning To Explore: A Comprehensive Study And Implementation Of Exploration-Based Reinforcement Learning
  • Distributional Reinforcement Learning with Score Functions
  • Seismic Arrival-time Picking on DAS Data with Deep 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              
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