Johannes Czech
Machine Learning Group, Computer Science Department, TU Darmstadt. Hochschulstrasse 1, Room S1|03 077, 64289 Darmstadt, Germany
+49 6151 16 22478 johannes (dot) czech (at) cs (dot) tu-darmstadt (dot) de
Meetings by appointment.

Mission. My research is centered around using deep learning models in planning algorithms such as the Monte-Carlo tree search.
We optimize our machine learning models in highly parallel reinforcement learning settings or with supervised learning.

Availability of thesis topics. Thank you for your interest and requests for thesis topics. Unfortunately, it is not possible for me to accept further applicants. Many thanks for your understanding.

Timeline.
2020 - now: Ph.D. student at the Machine Learning Lab, CS Department, TU Darmstadt, Germany
2017 - 2020: M.Sc. in computer science (visual computing) at TU Darmstadt, Germany
2014 - 2017: B.Sc. in computer science at Hochschule Furtwangen University, Germany

Supervised Theses.
2024 Tim Krieg, Exploring the Latest Neural Network Architecture Components in AlphaZero, B.Sc. Thesis, pdf
2024 Felix Helfenstein, Game phase specific models in AlphaZero, M.Sc. Thesis, co-supervision Jannis Blüml, arXiv, pdf
2024 Jinyao Chen, Evaluating Multi Policy Value Mont--Carlo Tree Search for Chess, M.Sc. Thesis
2023 Tam Truong, Monte Carlo Tree Search - Minimax Hybrid in AlphaZero, B.Sc. Thesis, pdf
2023 Martin Ruzicka, Utilizing Variance and Uncertainty in Monte-Carlo Tree Search, B.Sc. Thesis, pdf
2023 Markus Reuter, Nutzung der Neuartigkeit von Zuständen in Suchgraphen von AlphaZero, B.Sc. Thesis (German), pdf
2022 Anissa Manai, Creating an Agent for the Chess Variant Reconnaissance Blind Chess (RBC), M.Sc. Thesis, co-supervision Jannis Blüml
2022 Mika Pommeranz, Multimodal Learning for Chess, B.Sc. Thesis, co-supervision Jannis Blüml
2022 Rumei Ma, Continual Reinforcement Learning on TicTacToe, Connect4, Othello, Clobber and Breakthrough, B.Sc. Thesis
2022 Adrian Glauben, Replacing PUCT with a Planning Model, M.Sc. Thesis, co-supervision Jannis Blüml, pdf
2022 Jannik Holmer, Stochastic Exploration in Minimax Search by Using a Policy Predictor Network, B.Sc. Thesis, pdf
2022 Lorenz Leichthammer, Evaluating Planning-based Machine Learning Algorithms for Scheduling Railway Operations, M.Sc. Thesis, co-supervision Dr. Arturo Crespo, pdf
2021 Jan Frederik Liebig, Evaluating Population Based Reinforcement Learning for Transfer Learning, M.Sc. Thesis, pdf
2021 Dwarak Vittal, XmodRL: Explainable modular reinforcement learning, M.Sc. Thesis, co-supervision Quentin Delfosse, pdf, code
2021 Maximilian Alexander Gehrke, Assessing Popular Chess Variants Using Deep Reinforcement Learning, M.Sc. Thesis, pdf
2021 Jannis Ralf Joachim Blüml, Multi-Agent Reinforcement Learning and MCTS for Stratego, M.Sc. Thesis
2021 Maximilian Otte, Creating Emojis with Generative Adversarial Neural Cellular Automata, M.Sc. Thesis, co-supervision Quentin Delfosse
2021 Maximilian Langer, Evaluation of Monte-Carlo Tree Search for Xiangqi, B.Sc. Thesis, pdf
2021 Daniel Siersleben, Extending the Monte-Carlo Tree Search to an Ensemble Method using Teacher-Student Networks, B.Sc. Thesis
2020 Patrick Korus, An Evaluation of MCTS Methods for Continuous Control Tasks, B.Sc. Thesis

Supervised Courses and Projects.
WS 2023/24 Prof. Dr. Kristian Kersting, Jannis Blüml, Johannes Czech, Dr. Martin Mundt, Einführung in die Künstliche Intelligenz
WS 2022/23 Prof. Dr. Kristian Kersting, Jannis Blüml, Johannes Czech, Dr. Martin Mundt, Einführung in die Künstliche Intelligenz
WS 2021/22 Samuel Gajdos, Leif Schwaß, Gökay Karaahmetli, Lena-Marie Munderich, Daniel Creß, Yvonne Bihler (team management), LiGround v2 - Extending a Mordern Chess Variant Analysis GUI, B.Sc.-Praktikum, GitHub-Link, Website
WS 2021/22 Prof. Dr. Kristian Kersting, Johannes Czech, Jannis Weil, Praktikum aus Künstlicher Intelligenz, Creating an Agent to Play Pommerman
WS 2021/22 Prof. Dr. Ing . Uwe Klingauf, Prof. Dr. Kristian Kersting, Prof. Dr. Ing. Dipl. Wirtsch. Ing. Joachim Metternich, Prof. Dr. Ing. Matthias Weigold, Machine Learning Applications
WS 2021/22 Prof. Dr. Kristian Kersting, Karl Stelzner, Dr. Martin Mundt, Johannes Czech, Einführung in die Künstliche Intelligenz
SS 2021 Tillmann Rheude, Time Management in Chess with Neural Networks and Human Data, Student Research Project, pdf, code
SS 2021 Prof. Dr. Kristian Kersting, Data Mining und Maschinelles Lernen
WS 2020/21 Laurin Bielich, Jannik Holmer, Peter Mader, Simon Muchau, Martin Ruzicka, Hatice Irem Diril (team management), LiGround – A modern Chess Variant Analysis GUI for the 21st century, B.Sc.-Praktikum, GitHub-Link
WS 2020/21 Prof. Dr. Ing . Uwe Klingauf, Prof. Dr. Kristian Kersting, Prof. Dr. Ing. Dipl. Wirtsch. Ing. Joachim Metternich, Prof. Dr. Ing. Matthias Weigold, Machine Learning Applications
SS 2020 Prof. Dr. Kristian Kersting, Data Mining und Maschinelles Lernen
SS 2019 Prof. Dr. Johannes Fürnkranz , Praktikum aus Künstlicher Intelligenz, Learning to Play Bug­house


Publications


Publications can be found at DBLP, SemanticScholar