Member role: Industrial Relations Board
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.
The research of Massimo Fornasier embraces a broad spectrum of problems in mathematical modeling, analysis and numerical analysis. Fornasier is particularly interested in the concept of compression as appearing in different forms in data analysis, image and signal processing, and in the adaptive numerical solutions of partial differential equations or high-dimensional optimization problems.
Fornasier received his doctoral degree in computational mathematics in 2003 from the University of Padua, Italy. After spending from 2003 to 2006 as a postdoctoral research fellow at the University of Vienna and University of Rome La Sapienza, he joined the Johann Radon Institute for Computational and Applied Mathematics (RICAM) of the Austrian Academy of Sciences where he served as a senior research scientist until March 2011. He was an associate researcher from 2006 to 2007 for the Program in Applied and Computational Mathematics of Princeton University, USA. In 2011 Fornasier was appointed Chair of Applied Numerical Analysis at TUM. He is a member of VQR, a panel responsible for the evaluation of the quality of research in Italy. He is also a member of the editorial boards of Networks and Heterogeneous Media, Journal of Fourier Analysis and Applications and Calcolo.
Carsten Rother received his diploma degree with distinction in 1999 from the University of Karlsruhe/Germany, and his PhD degree in 2003 from the Royal Institute of Technology (KTH) Stockholm/Sweden. From 2003 until 2013 he was researcher in the Computer Vision Group at Microsoft Research Cambridge/UK, lead by Andrew Blake. From 2014 until 2017 he was full Professor at TU Dresden/Germany. Since 2017 he is full Professor at Uni Heidelberg/Germany, heading the Computer Vision and Learning Lab. He is also coordinating director of the Heidelberg Collaboratory for Image Processing (HCI) and an AI consultant for companies. In the last years, two start-ups emerged from his lab https://rabbitai.de/ and https://www.copresence.tech/. His research interests are in the field of computer vision and machine learning, including related fields such as human-computer interaction and computational imaging. He has been working on a broad range of applications, such as image generation, image editing (incl. image segmentation, alpha matting, and deconvolution), image matching (incl. stereo matching, and scene flow), scene understanding (incl. 6D object pose estimation), and Bio-Imaging (incl. cell tracking). He has published over 270 articles (current H-index 93) at international conferences and journals. He received eight awards, including awards at CVPR ’13, CVPR ’05, CHI ’07, BMVC ’16 and ACCV ’14. He was awarded the DAGM Olympus prize in 2009. He has co-developed two Microsoft Products, GrabCut for Office 2010 and AutoCollage. He received prestigious funding awards, such as an ERC Consolidator Grant. He serves as area chair for major conferences and he has been associated editor for T-PAMI.