Curriculum Vitae¶
Education¶
| Years | Degree |
|---|---|
| 2021 – 2025 | Dr. rer. nat. (Summa cum laude), School of Computation, Information and Technology, Technical University of Munich, Germany. Thesis: Generalized Neural Wave Functions Advisor: Prof. Stephan Günnemann 2nd Examiner: Prof. Philipp Grohs |
| 2018 | Erasmus Exchange Semester, University College Dublin, Ireland. Focus: Machine Learning, Data Visualization |
| 2017 – 2021 | M.Sc. in Computer Science, Technical University of Munich, Germany. Grade average: 1.2 (top 10%) – passed with high distinction Thesis: Fast and Flexible Temporal Point Processes using Triangular Maps Advisor: Prof. Stephan Günnemann Deutschlandstipendium recipient (2019/20) |
| 2014 – 2017 | B.Sc. in Computer Science, Technical University of Brunswick, Germany. Grade average: 1.3 (top 10%) Thesis: Visualizing the IoT with Mixed Reality Advisors: Prof. Felix Büsching, Prof. Lars Wolf Deutschlandstipendium recipient (2016/17) |
Professional Experience¶
Research Scientist — Cusp AI¶
May 2025 – Present, Berlin, Germany
Team lead: Prof. Max Welling
Advisory Committee: Nobel Laureate Prof. Geoffrey Hinton, Turing Award Winner Prof. Yann LeCun, Prof. Kristin Persson, Prof. Aron Walsh
- Accelerating atomistic Monte Carlo and molecular dynamics simulations with machine learning
- Development of a novel GPU-native simulation framework
Research Scientist Intern — Microsoft Research AI for Science¶
April 2023 – July 2023, Berlin, Germany
Team lead: Prof. Frank Noe, Supervisor: Dr. Jan Hermann
- Research on foundational neural network wave function architecture
- Developed folx library
Research Associate — Data Analytics & Machine learning, TUM¶
February 2021 – May 2025, Germany
Supervisor: Prof. Stephan Günnemann
- Research on deep learning for atomistic and quantum simulations
- Teaching: lecture, seminar, practical courses (up to 1,000 students per term)
- Setup and administration of in-house GPU cluster (100+ GPUs, 23 servers)
- Development and maintenance of SEML
Research Working Student — German Aerospace Center (DLR)¶
June 2020 – September 2020, Köln, Germany
Team lead: Dr. Tobias Stollenwerk
- Research on neural network wave functions for periodic systems
- Publication in Physical Review B (2023)
Research Scientist Intern — NASA Quantum AI Lab¶
September 2019 – February 2020, Mountain View, CA, USA
Team lead: Dr. Eleanor Rieffel
- Research on deep learning for quantum mechanics
- Quantum computing for deep learning integration
- Publications at SIGKDD 2020 and Arxiv 2021
Machine Learning Engineering Working Student — Artisense GmbH¶
January 2019 – August 2019, Munich, Germany
Team lead: Prof. Daniel Cremers
- Research on uncertainty-aware ML-based real-time SLAM algorithms
Research Assistant — Computer Graphics Lab, Technical University of Brunswick¶
November 2015 – August 2017, Germany
Team lead: Prof. Marcus Magnor
- Research on classical and VR-based information visualization
Awards¶
Spotlight Presentation, ICML 2026 — Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State
Best Poster Award, ChemAI 2025 — Learning Equivariant Non-Local Electron Density Functionals- Spotlight Presentation, ICLR 2025 Workshop on Multiscale Processes — On Learning Quasi-Lagrangian Turbulence
- Spotlight Presentation, ICLR 2025 — Learning Equivariant Non-Local Electron Density Functionals
- Best Paper Runner-up, GRaM ICML 2025 — Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
- Oral Presentation, NeurIPS 2024 — Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations
- Spotlight Presentation, ICLR 2022 — Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
- Oral Presentation, NeurIPS 2020 — Fast and Flexible Temporal Point Processes using Triangular Maps
- Oral Presentation, SIGKDD 2020 Research Track — High-Dimensional Similarity Search with Quantum-assisted VAE
Deutschlandstipendium recipient (2016/17, 2019/20).
Service¶
Organizing Committee
Blog Post Track of ICLR (2025, 2026)
Reviewer
Nature Machine Intelligence, AISTATS (2024), NeurIPS (2022, 2023, 2024, 2025, 2026), ICLR (2025, 2026), ICML (2023, 2024, 2025, 2026), LoG (2024)