cv

Ph.D. Student in Machine Learning

Technical University of Munich (TUM), Helmholtz Munich

Collaboration: Contribute to the Causal Cell Dynamics international lab (Helmholtz Munich, MILA Montreal). Develop methods for causally structured deep representation learning to improve our understanding of cellular decisions.

• Self-Supervised Learning and Foundation Models
• Generative Models and Flow Matching
• Neural Differential Equations
• Co-Organizer Learning Meaningful Representations of Life (LMRL) Workshop at ICLR 2025 (Singapore)
• Co-Organizer Explainable ML Workshop at ECCB 2024 (Finland)
• Talk at Helmholtz AI 2024 (Germany)
• Poster at NeurIPS 2022 (USA)
• Attended Oxford ML Summer School 2022 (UK), Advanced Course on Data Science and ML 2022 (Italy)
• Teaching MSc-level: Statistical Learning (SS24), Deep Learning Seminar (WS21-WS24, Organizer since WS24)
• Teaching BSc-level: Analysis for Informatics (WS23-WS24)
• Supervision of interns, working- and thesis students

Volunteer

KI macht Schule!

Hold workshops for high-school students on artificial intelligence.

Ph.D.

Technical University of Munich (TUM)

Machine Learning

M.Sc.

Technical University of Munich (TUM)

Robotics, Cognition, Intelligence

B.Sc.

Leibniz Universität Hannover

Engineering and Business Administration

CFGen: Generating Multi-Modal and Multi-Attribute Single-Cell Counts

Flow-matching generative model for single-cell data augmentation.

Delineating the Effective Use of Self-Supervised Learning in Single-Cell Genomics

Benchmarking self-supervised learning methods for cell type prediction.