Member role: Academic Fellow
Matthias Hein is Bosch endowed Professor of Machine Learning and the coordinator of the international master program in machine learning at the University of Tübingen. He is member of the Excellence Cluster “Machine Learning: New Perspectives for Science” and the Tübingen AI Center. His main research interests are to make machine learning systems robust, safe and explainable and to provide theoretical foundations for machine learning, in particular deep learning. He serves regularly as area chair for ICML, NeurIPS or AISTATS and has been action editor for Journal of Machine Learning Research (JMLR) from 2013 to 2018. He is an ELLIS Fellow and has been awarded the German Pattern recognition award, an ERC Starting grant and several best paper awards (NeurIPS, COLT, ALT).
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.
Abhinav Valada is a Full Professor (W3) at the University of Freiburg, where he directs the Robot Learning Lab. He is a member of the Department of Computer Science, the BrainLinks-BrainTools center, and a founding faculty of the ELLIS unit Freiburg. Abhinav is a DFG Emmy Noether AI Fellow, Scholar of the ELLIS Society, IEEE Senior Member, and Chair of the IEEE Robotics and Automation Society Technical Committee on Robot Learning.
He received his PhD (summa cum laude) working with Prof. Wolfram Burgard at the University of Freiburg in 2019, his MS in Robotics from Carnegie Mellon University in 2013, and his BTech. in Electronics and Instrumentation Engineering from VIT University in 2010. After his PhD, he worked as a Postdoctoral researcher and subsequently an Assistant Professor (W1) from 2020 to 2023. He co-founded and served as the Director of Operations of Platypus LLC from 2013 to 2015, a company developing autonomous robotic boats in Pittsburgh, and has previously worked at the National Robotics Engineering Center and the Field Robotics Center of Carnegie Mellon University from 2011 to 2014.
Abhinav’s research lies at the intersection of robotics, machine learning, and computer vision with a focus on tackling fundamental robot perception, state estimation, and planning problems to enable robots to operate reliably in complex and diverse domains. The overall goal of his research is to develop scalable lifelong robot learning systems that continuously learn multiple tasks from what they perceive and experience by interacting with the real world. For his research, he received the IEEE RAS Early Career Award in Robotics and Automation, IROS Toshio Fukuda Young Professional Award, NVIDIA Research Award, AutoSens Most Novel Research Award, among others. Many aspects of his research have been prominently featured in wider media such as the Discovery Channel, NBC News, Business Times, and The Economic Times.
Michael Black is a founding director at the Max Planck Institute for Intelligent Systems, an Honorarprofessor at the University of Tübingen, and is the Speaker of Cyber Valley. Black received his B.Sc. from the University of British Columbia (1985), M.S. from Stanford (1989), and Ph.D. from Yale University (1992). He has held positions at the University of Toronto, Xerox PARC, Brown University, and Amazon. He is a recipient of the PAMI Distinguished Researcher Award, the 2022 and 2010 Koenderink Prize, the 2013 Helmholtz Prize, the 2020 Longuet-Higgins Prize, and the 2024 SIGGRAPH Asia Test-of-Time award. He is a member of two national academies (Germany and Sweden). He has co-founded two companies: Body Labs and Meshcapade.
Bernhard Schölkopf’s scientific interests are in machine learning and causal inference. He has applied his methods to a number of different fields, ranging from biomedical problems to computational photography and astronomy. Bernhard studied physics and mathematics and earned his Ph.D. in computer science in 1997, becoming a Max Planck director in 2001. He has (co-)received the Berlin-Brandenburg Academy Prize, the Royal Society Milner Award, the Leibniz Award, the BBVA Foundation Frontiers of Knowledge Award, and the ACM AAAI Allen Newell Award. He is Fellow of the ACM and of the CIFAR Program “Learning in Machines and Brains”, a member of the German Academy of Sciences, and a Professor at ETH Zurich. He helped start the MLSS series of Machine Learning Summer Schools, the Cyber Valley Initiative, the ELLIS society, and the Journal of Machine Learning Research, an early development in open access and today the field’s flagship journal. In 2023, he founded the ELLIS Institute Tuebingen, and acts as its scientific director.
Ulrike von Luxburg is a full professor for the Theory of Machine Learning at the University of Tübingen, Germany. Her research analyzes machine learning algorithms from a theoretical point of view, tries to understand their implicit mechanisms, and to give formal statistical guarantees for their performance. In this way, she reveals fundamental assumptions, biases, strenghts and weaknesses of widely used machine learning algorithms, for example in the field of explainable machine learning. Next to her own research group, she is coordinating a large research consortium on Machine Learning in Science and the Tübingen CZS Institute for AI and Law. Together with an interdisciplinary team of students, she developed and implemented an interactive exhibition “Artificial Intelligence in Tübingen” in the City Museum Tübingen, which received Germany’s highest award for science communication in 2024. She has chaired some of the most important machine learning conferences such as NeurIPS and COLT, and her research won many best paper awards (see below for a list). She is a member of the German National Academy of Sciences.