معرفی
Thomas Schnake is a postdoctoral researcher at the Machine Learning Lab of the Technical University of Berlin and the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds a Ph.D. in Machine Learning from TU Berlin and prior degrees in Mathematics and Scientific Computing from Humboldt University of Berlin. His research focuses on Explainable AI (XAI), Natural Language Processing, and the mathematical foundations of machine learning. He has also gained industry experience at ebuero AG and GFaI e.V. in Berlin.
Education:
- B.Sc. Mathematics & Philosophy, Humboldt University Berlin (2014)
- M.Sc. Mathematics, Humboldt University Berlin (2018)
- M.Sc. Scientific Computing, Technical University Berlin (2018)
- Ph.D. Machine Learning, Technical University Berlin (2024)
Research Interests include:
- Explainable AI for complex domains like quantum chemistry and histopathology
- Graph neural network interpretability through walk-based explanations
- High-resolution data synthesis with minimal input
- Unsupervised anomaly detection in text and energy systems
His recent publications (2021-2025) demonstrate contributions to XAI frameworks, graph neural network explanations, and transformer model interpretability. He is affiliated with two prominent institutions and maintains active research in interdisciplinary areas combining mathematics and machine learning.
