Location: Berlin

David Jacob Drexlin is a Master’s student in Computer Science at the Technical University of Berlin, where he conducts research with the Machine Learning Group. He spent a semester as a visiting student at Ewha Womans University in Seoul and held internships at IBM Research & Development in quantum computing and machine learning. His research focuses on probabilistic generative modeling—particularly incorporating metadata into diffusion models—and he has also worked on self‑supervised learning. He is finalizing his Master’s thesis on diffusion approaches for histopathological image augmentation, and a paper based on this work is under peer review. One of his early publications, “Quantenspiele, IBMs Qiskit und RasQberry” (Heise, 2021), explored quantum game theory on real quantum hardware. He is supported by a scholarship from the Konrad Zuse School of Excellence in Learning and Intelligent Systems.

I am a PhD student of the ELIZA Zuse School in Berlin, where I am working with Frank Noé and Klaus-Robert Müller on AI4Science. My research focuses on generative models for dynamical biophysical systems. I also just love geeking out about all things computer science — from machine learning and physics to weird algorithms and cool hacks.

I did my bachelor’s degree in computer science at the University of Jena. My bachelor thesis was in the area of machine learning, in particular variational inference and some graph theory. During my Bachelor’s, I also served as a tutor for discrete mathematics. I am currently pursuing a Master’s degree in Computer Science at TU Berlin, focusing on machine learning and data analytics.

Anika Merklein is a computer scientist with a background in social work, currently pursuing her master’s degree in computer science with a focus on machine learning at TU Berlin. Previously, she worked as a research software developer at the Max Planck Institute for the History of Science in Berlin, where she developed digital methods for historical science, focusing on machine learning for the humanities, research data management, and semantic modeling. Her current research focuses on unsupervised methods to analyze stylistic elements in early modern book illustrations, employing Vision Transformers and Explainable AI techniques to gain insights from low-resource data contexts.
In addition, she teaches at the University of Applied Sciences on topics related to social power relations and computer technology. She is particularly interested in how social norms and ideas influence the research and development of technology.

Wojciech Samek is a professor at TU Berlin, head of the AI Department at Fraunhofer HHI, and a fellow at BIFOLD and ELLIS Unit Berlin. His research focuses on explainable AI (XAI), covering method development, theoretical studies, and applications in medicine and geoscience. His pioneering contributions to XAI include key methods like Layer-wise Relevance Propagation (LRP) [1] and advancements in concept-level explainability, evaluation of explanations, and XAI-based model- and data improvement.

He has edited two books on XAI, served as a senior editor for IEEE TNNLS and associate editor for various other journals, and held area chair roles at NeurIPS, ICML, and NAACL. He is a member of Germany’s Platform for AI and serves on the boards of AGH University’s AI Center, HEIBRiDS, and ELIZA. He has received multiple best paper awards, including from Pattern Recognition (2020), Digital Signal Processing (2022), and the IEEE Signal Processing Society (2025).

[1] Bach, S, Binder, A, Montavon, G, Klauschen, F, Müller, K-R, Samek, W (2015). On Pixel-wise Explanations for Non-Linear Classifier Decisions by Layer-wise Relevance Propagation. PLOS ONE, 10(7):e0130140.

Jacky Behrendt is a Master’s student in Mathematics at Technische Universität Berlin and a member of the Konrad Zuse School ELIZA. She works as a Student Assistant at the Intelligent Biomedical Sensing (IBS) Lab at TU Berlin and BIFOLD, where she develops machine learning tools for biomedical image sensing. Her research focuses on blind source separation methods—particularly independent component analysis (ICA)—for functional near-infrared spectroscopy (fNIRS) data analysis.

She has contributed to Cedalion, a toolbox for fNIRS signal analysis and synthetic data generation, and co-authored an international study on variability in fNIRS analysis. Jacky also collaborates with researchers in Boston on applying ICA to neuroimaging data and is actively involved in advancing signal processing techniques for fNIRS applications.

I am currently a Student Assistant at the Machine Learning Group of TU Berlin, where I contribute to research in computer vision and self-supervised learning. My research focuses on domain shift in self-supervised models, particularly in medical image processing. I have recently explored this area to better understand how domain shifts impact model performance.

Marc Toussaint is professor for Intelligent Systems at TU Berlin and was previously professor for ML and Robotics at U Stuttgart, Max Planck Fellow at the MPI for Intelligent Systems, and visiting scholar at MIT. His research interests are in the intersection of AI and robotics, namely in using machine learning, optimization, and AI reasoning to tackle fundamental problems in robotics. Concrete research topics include models and algorithms for physical reasoning, task-and-motion planning (logic-geometric programming), learning heuristics, the planning-as-inference paradigm, and learning to transfer model-based strategies to reactive and adaptive real-world behavior.

Konrad Rieck is a Professor of Computer Science at TU Berlin, where he leads the Chair of Machine Learning and Security within the Berlin Institute for the Foundations of Learning and Data. Before joining TU Berlin, Konrad Rieck held positions at TU Braunschweig, the University of Göttingen, and the Fraunhofer Institute FIRST. He was also a Guest Professor at TU Wien in 2024. His research focuses on the intersection of computer security and machine learning, with particular emphasis on developing novel techniques for detecting attacks, analyzing malicious software, and uncovering vulnerabilities. He also investigates the security and privacy of learning algorithms as well as efficient methods for analyzing structured data, such as strings, trees, and graphs. His work has been recognized with several distinctions, including a Google Faculty Research Award, the German IT-Security Award, and an ERC Consolidator Grant.