I am a second-year PhD candidate at the Institute of Mathematics, TU Berlin and a research associate at Zuse Institute Berlin (ZIB). Under the supervision of Prof. Sebastian Pokutta, I am currently part of the Interactive Optimization and Learning (IOL) research group at ZIB. Additionally, I am a member of the Berlin Mathematical School (BMS), which is part of the Math+ Excellence Cluster.
My current research focuses on the Interpretability of Deep Neural Networks. In particular, I am investigating the use of saliency maps created by feature selectors in a min-max game to gain valuable insights into the inner workings of black-box models.
Prior to joining IOL in 2022, I completed my Master’s thesis at the Fraunhofer Heinrich-Hertz Institute, where I worked on Deep Hybrid Discriminative-Generative Modeling. Specifically, I investigated the joint optimization of Variational Autoencoders (VAEs) and Residual Networks (ResNets) and analyzed the Out-of-Distribution detection behavior of these models in computer vision tasks.
- Interpretability Guarantees with Merlin-Arthur ClassifiersIn Artificial Intelligence and Statistics (AISTATS), 2024
- Extending Merlin-Arthur Classifiers for Improved InterpretabilityIn Joint Proceedings of the xAI-2023 Late-breaking Work, Demos and Doctoral Consortium, co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023), Jul 20236-page extended abstract. Awarded Best Doctoral Proposal at xAI-2023.
- Robustness of Hybrid Discriminative-Generative ModelsTechnical University of Berlin, Jul 2022
- Modeling and Simulation of Convective Flows in the Outer Earth’s Core using the Finite Element MethodTechnical University of Berlin, Jul 2018