
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.


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 |
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 |
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 |