About Me

• My name is Tim and I am a PhD student at Saarland University, Germany. I am broadly interested in the field of artificial intelligence and machine learning. Ultimately, I would love to contribute to the development of artificial general intelligence (AGI) systems.
  
  I have a vast theoretical foundation in machine learning, covering general principles, deep learning, reinforcement learning, optimization, trustworthiness, and explainability. I also have some basic knowledge of structural causal models, causal discovery, and variational inequality methods (e.g., used in multi-agent systems and GANs). On the symbolic side of AI, I know about heuristic exploration of search spaces, SAT and SMT solving, deductive verification, and model checking. Regarding programming, I have extensive experience in Python, especially in PyTorch, and related libraries like PyTorch Lightning. I have also used languages like C, C++, Java, and JavaScript throughout my studies.
  
  Currently, I am working with Prof. Isabel Valera and Prof. Jilles Vreeken on event sequences, time series analysis, and attention-based models.

Education

• Saarland University, Saarbrücken, Germany
  PhD student in Machine Learning under Prof. Isabel Valera | 2025 – now

• Saarland University, Saarbrücken, Germany
  Computer Science, M.Sc. (Graduate School Student) | 2023 – 2025
  Thesis: SeqRuleNet: Neuro-Symbolic Discovery of Sequential Rule Sets (supervised by Prof. Isabel Valera and Prof. Jilles Vreeken)
  Final GPA: 1.0 with distinction (best: 1.0, worst: 4.0; American: 4.0)

• Saarland University, Saarbrücken, Germany
  Data Science and Artificial Intelligence, B.Sc. | 2020 – 2023
  Thesis: What makes you say that? Explaining Transformers using Rule Mining and Input Prototyping (supervised by Prof. Jilles Vreeken)
  Final GPA: 1.0 (best: 1.0, worst: 4.0; American: 4.0)

Teaching

• Course Assistant, Elements of Machine Learning (Advanced Lecture)
  Saarland University, Germany | Oct. 2026 – Mar. 2027

• Course Assistant, Machine Learning (Core Lecture)
  Saarland University, Germany | Apr. 2026 – Sep. 2026

• Course Assistant, Elements of Machine Learning (Advanced Lecture)
  Saarland University, Germany | Oct. 2025 – Mar. 2026

• Instructor, Advanced Time Series Analysis (Seminar)
  Saarland University, Germany | Apr. 2025 – Sep. 2025

• Lecturer & Coach, Programming 2 Preparatory Course
  Saarland University, Germany | Apr. 2025

• Instructor, Tutor Didactics Seminar
  Saarland University, Germany | Oct. 2024

• Lecturer & Coach, Math Preparatory Course
  Saarland University, Germany | Sep. 2023 – Oct. 2023

• Teaching Assistant, Machine Learning (Core Lecture)
  Saarland University, Germany | Mar. 2023 – Sep. 2023

• Head Teaching Assistant, Programming 1 (Basic Lecture)
  Saarland University, Germany | Oct. 2022 – Mar. 2023

• Head Teaching Assistant & Lecturer & Coach, Math Preparatory Course
  Saarland University, Germany | Sep. 2022 – Oct. 2022

• Teaching Assistant, Programming 2 (Basic Lecture)
  Saarland University, Germany | Mar. 2022 – Sep. 2022

• Teaching Assistant, Programming 1 (Basic Lecture)
  Saarland University, Germany | Oct. 2021 – Mar. 2022

• Teaching Assistant, Math Preparatory Course
  Saarland University, Germany | Sep. 2021 – Oct. 2021

Scholarships

• Konrad Zuse School of Excellence in Learning and Intelligent Systems (ELIZA)
  Preparatory Phase (Master) Scholarship | Oct. 2024 – Sep. 2025

• German Academic Scholarship Foundation
  In German: Studienstiftung des deutschen Volkes, Student Scholarship | Mar. 2023 – Sep. 2025

• Graduate School of Computer Science
  Preparatory Phase (Master) Scholarship | Nov. 2023 – Sep. 2024

Publications

• Bauerschmidt, T. N., Valera, I., and Vreeken, J. What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rules. In 40th Conference on Neural Information Processing Systems (NeurIPS 2026), 2026.

  Putrich, V. S., Bauerschmidt, T. N., Jain, C., Lin, S., Hoffmann, J., and Valera, I. Formula Neural Networks as Action Policies in Safety-Critical Planning: An Empirical Evaluation. In 19th International Symposium on Combinatorial Search, 2026.

  Koyuncu, B., Bauerschmidt, T. N., and Valera, I. E-ProTran: Efficient Probabilistic Transformers for Forecasting. In ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling, 2024.