Timo P. Gros

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About me

I am a Ph.D. student in the Modeling and Simulation Group at the Computer Science Department of Saarland University and a member of the Graduate School of Computer Science.

Publications

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2021

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• DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning to appear.
  Proceedings of the 18th International Conference on Quantitative Evaluation of SysTems (QEST’21).
  Joint work with Daniel Höller, Jörg Hoffmann, Michaela Klauck, Hendrik Meerkamp, and Verena Wolf.

• Debugging a Policy: A Framework for Automatic Action Policy Testing.
  Proceedings of the Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), at ICAPS’21.
  Joint work with Marcel Steinmetz, Philippe Heim, Daniel Höller, and Jörg Hoffmann.

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2020

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• Lab Conditions for Research on Explainable Automated Decisions (Position Paper)
  Post-Proceedings of the Workshop on Foundations of Trustworthy AI - Integrating Learning, Optimization and Reasoning (TAILOR’20).
  Joint work with Christel Baier, Maria Christakis, David Groß, Stefan Gumhold, Holger Hermanns, and Michaela Klauck.

• Real-time Decision Making For a Car Manufacturing Process Using Deep Reinforcement Learning.
  Proceedings of the 2020 Winter Simulation Conference.
  Joint work with Joschka Groß and Verena Wolf.

• Tracking the Race Between Deep Reinforcement Learning and Imitation Learning.
  Proceedings of the 17th International Conference on Quantitative Evaluation of SysTems (QEST’20).
  Joint work with Daniel Höller, Jörg Hoffmann, and Verena Wolf.

• Tracking the Race Between Deep Reinforcement Learning and Imitation Learning – Extended Version.
  Joint work with Daniel Höller, Jörg Hoffmann, and Verena Wolf.

• TraceVis: Towards Visualization for Deep Statistical Model Checking.
  Proceedings of the 9th International Symposium On Leveraging Applications of Formal Methods, Verification and Validation (ISoLA’20)
  Joint work with David Groß, Stefan Gumhold, Jörg Hoffmann, Michaela Klauck, and Marcel Steinmetz.

• Deep Statistical Model Checking.
  Proceedings of the 40th International Conference on Formal Techniques for Distributed Objects, Components, and Systems (FORTE’20), 2020. Joint work with Holger Hermanns, Jörg Hoffmann, Michaela Klauck, and Marcel Steinmetz.

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2019

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• aMAZEing Programming – Providing SKILLs to Fellow Students.
  SKILL 2019 - Studierendenkonferenz Informatik Joint work with Pascal L. Held, Pascal Lauer, Niklas O. Metzger, and Kallistos Weis.

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Theses

• Markov Automata taken by Storm, scientific work for studying (high school) teaching & Bachelor thesis, January 2018
• Tracking the Race: Analyzing Racetrack Agents Trained with Imitation Learning and Deep Reinforcement Learning, May 2021

Co-supervised Students

• Lukas Schaller, research assistant, 2021.
  Using Deep Reinforcement Learning for Conjunctive Port Mappings
• Tristan Schneider, Bachelor thesis, ongoing.
  Motion Primitives for Motion Planning with Reinforcement Learning
• Ilka Kopping, Master thesis, ongoing.
• Christian Closheim, Bachelor thesis, ongoing.
• Joschka Groß, Master thesis, 2021.
  Using Deep Reinforcement Learning to Optimize Assignment Problems
• Luis Paulus, Bachelor thesis, 2021.
  Mapping Instructions to Ports: A Reinforcement Curriculum Learning Approach
• Hendrik Meerkamp, Master thesis, 2020.
  Deep Statistical Model Refinement
• Roman Joeres: Bachelor thesis, 2020.
  Multiple Sequence Alignment using Deep Reinforcement Learning
• Anurag Das: Research Immersion Lab, 2020.
  Real-time Decision Making For a Car Manufacturing Process Using Hierarchical Reinforcement Learning
• Jeremias Wagner: Bachelor thesis, 2020.
  Deep Reinforcement Learning to Battle Snake
• Paul Krieger: Bachelor thesis, 2020.
  Explainable Classification for an Application System​

Teaching

• Winter 13 : Tutor for Programming 1 at the Dependable Systems and Software group
• Winter 14 : Tutor for Mathematics for Engineers 1 at the Chair for Numerical Mathematics
• Summer 15 : Tutor for Programming 2 at the Compiler Design Lab
• Winter 15 : Tutor for Basics of Algorithms and Data structures at the chair for Computational Complexity
• Summer 16 : Supervision-tutor for Programming 2 at the Compiler Design Lab
• Summer 17 : Supervision-tutor for Programming 2 at the Compiler Design Lab
• Summer 18 : Assistant for Concurrent Programming at the group of Modeling and Simulation
• Summer 19 : Assistant for Probabilistic Models and Data Analysis
• Winter 19: Assistant for Rein4cement Learning
• Summer 20: Assistent for Statistics Lab
• Summer 21: Assistent for Concurrent Programming
• Summer 21: Assistent for Deep Reinforcement Learning

Other Work

I have reviewed manuscripts for ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS) 2020, Quantitative Evaluation of SysTems (QEST) 2020 and 2021, and 7th IFAC Conference on Analysis and Design of Hybrid Systems (ADHS) 2021.