PhD Candidates

  • Hugo Max Martin, Model-based Offline Reinforcement Learning, TU Eindhoven

Master Thesis

  • (Ongoing) Rick van Oosterhout, Multi-fidelity Simulation Optimization for Large-scale Problems, TU Eindhoven
  • (Ongoing) Yana Stoyanova, Model Transfer for Offline Reinforcement Learning, TU Eindhoven
  • (Ongoing) Ivan Knunyants, Algorithm-hardware co-optimization for Transformers, TU Eindhoven, in collaboration with imec
  • (Ongoing) Nicholas Kölln, Offline Reinforcement Learning via Transformers, TU Eindhoven
  • (2023) Maiko Bergman, Offline Reinforcement Learning, TU Eindhoven
  • (2023) Cas Teeuwen, Delivery Planning via Reinforcement Learning, TU Eindhoven, in collaboration with Datacation
  • (2023) Ibrahim El Garmouhi, Robust Counterfactual Model Explanation for Patient-Centric Life-style Recommendation on the Cloud, TU Eindhoven, in collaboration with dr. Mostafa Kia
  • (2022) Youri Vis, Effective Sampling in Intrinsically Motivated Reinforcement Learning, TU Eindhoven
  • (2022) Jeroen Albrechts, Hyperparameter Optimization via Model-Based Reinforcement Learning, TU Eindhoven
  • (2022) Mathijs Boezer, FastDTI: Drug-Target Interaction Prediction using Multimodality and Transformers, TU Eindhoven, Accepted at NLDL 2023
  • (2022) Çağla Sözen, A comparative study on Unsupervised Deep Learning Methods for X-Ray Image denoising with Multi-Image Self2Self and Single Frequency, Denoising, TU Eindhoven, in collaboration with Philips Research
  • (2021) Stef Creemers, Balancing Efficiency and Fairness on Ride-Hailing Platforms via Reinforcement Learning, TU Eindhoven
  • (2021) Zian Fang, Long- and Short-term Sequential Recommendation with Enhanced Temporal Self-attention, TU Eindhoven
  • (2021) Huiyao Wu, MuseBar: Alleviating Posterior Collapse in VAE Towards Music Generation, TU Eindhoven, Published at IDA 2022
  • (2021) Erik Ussin, Enhanced Soft Actor Critic for off-policy continuous control
  • (2019) Alper Bate, Topic-Sensitive Abstractive Text Summarization using a Variational Autoencoder
  • (2019) Hermann Foot, Adaptive Packing Optimization using Reinforcement and Imitation Learning
  • (2017) Ekaterina Dolgovykh, Factorization Machines models for Click-through Rate prediction under data sparsity, in collaboration with XING
  • (2013) Mario Herdt, Personalized Diverse Recommendations, TU Darmstadt

Bachelor Thesis

  • (Ongoing) Daan Gouwy, Model-based Offline Reinforcement Learning, TU Eindhoven
  • (Ongoing) Pradyut Nair, Model-based Offline Reinforcement Learning, TU Eindhoven
  • (2022) Yana Stoyanova, Comparison Between Various Ensemble Uncertainty Quantification Methods in Regression Tasks, TU Eindhoven, Published at AAAI 2023
  • (2022) Lieve Göbbels, Unreliable Uncertainty: How Bayesian Non-identifiability Influences the Performance of Uncertainty Quantification Methods in the Context of Reinforcement Learning, TU Eindhoven
  • (2016) Akath Singh Dua, Sequential Recommendation Systems
  • (2015) Radhika Gaonkar, MDP-based itinerary recommendation using geo-tagged social media, TU Darmstadt, Published at IDA 2018
  • (2014) Divya Venugopalan, Tensor Factorization for User Purchase Pattern Recognition, TU Darmstadt
  • (2014) Qifeng Hu, Recommender Systems and Personalization, TU Darmstadt