Focus area: ML Systems

Grégoire Montavon is a Professor at Charité – Universitätsmedizin Berlin and a Research Group Lead in the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He received a Master’s degree in Communication Systems from the École Polytechnique Fédérale de Lausanne in 2009 and a Ph.D. in Machine Learning from the Technische Universität Berlin in 2013. He is a member of the ELLIS Unit Berlin, and an editorial board member of Pattern Recognition. He is recipient of the 2020 Pattern Recognition Best Paper Award. His research interests include explainable machine learning, deep neural networks, and unsupervised learning. His research focuses on methods of Explainable AI (XAI) and applications in medicine.

Ola Engkvist, PhD, is Executive Director and Head of Molecular AI within Discovery Sciences at AstraZeneca R&D, where he leads the development and application of machine learning and artificial intelligence to accelerate drug design. He has published over 180 peer-reviewed scientific articles and is an ELLIS fellow at the European Laboratory for Learning and Intelligent Systems and a 2025 Clarivate Highly Cited Researcher. He holds an adjunct professorship in machine learning and AI for drug design at Chalmers University of Technology, serves as a Trustee of the Cambridge Crystallographic Data Centre, and is recognized for his work in pharmaceutical innovation.

Matthias W. Seeger received a Ph.D. from the School of Informatics, Edinburgh university, UK, in 2003 (advisor Christopher Williams). He was a research fellow with Michael Jordan and Peter Bartlett, University of California at Berkeley, from 2003, and with Bernhard Schoelkopf, Max Planck Institute for Intelligent Systems, Tuebingen, Germany, from 2005. He led a research group at the University of Saarbruecken, Germany, from 2008, and was assistant professor at the Ecole Polytechnique Federale de Lausanne from fall 2010. He joined Amazon as machine learning scientist in 2014. He received the ICML Test of Time Award in 2020.

I am an ELLIS PhD student in the Medical Image Computing department at the German Cancer Research Center (DKFZ). Before starting my PhD, I worked as a Junior Data Scientist at AstraZeneca in Gothenburg (Sweden) and as a Research Assistant at Uppsala University.

My current research focuses on improving model generalizability across clinical settings, with a particular emphasis on brain imaging. I am developing a stroke identification algorithm designed to perform robustly in multi-centric environments. I am also collaborating with the University of Amsterdam on analyzing temporal brain image data to extract patterns that enhance model generalization.

I have received several travel grants for international research stays during my PhD, as well as excellence scholarships during my Bachelor’s and Master’s studies.

PhD student, AutoML Freiburg

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.

Christian Theobalt is The Scientific Director of the Visual Computing and Artificial Intelligence Department at the Max-Planck-Institute for Informatics, Saarbrücken, Germany. He is also a Professor of Computer Science at Saarland University, Germany. Christian is also the Founding Director of the Saarbrücken Research Center for Visual Computing, Interaction and Artificial Intelligence (VIA), a strategic research partnership between Google and MPI for Informatics. From 2007 until 2009 he was a Visiting Assistant Professor in the Department of Computer Science at Stanford University. He received his MSc degree in Artificial Intelligence from the University of Edinburgh, his Diplom (MS) degree in Computer Science from Saarland University, and his PhD (Dr.-Ing.) from the Max-Planck-Institute for Informatics. In his research he looks at algorithmic problems that lie at the intersection of Computer Graphics, Computer Vision and Machine Learning, such as: static and dynamic 3D scene reconstruction, neural rendering and neural scene representations, marker-less motion and performance capture, virtual humans, virtual and augmented reality, computer animation, intrinsic video and inverse rendering, computational videography, machine learning for graphics and vision, visual generative AI, new sensors for 3D acquisition, as well as image- and physically-based rendering. He is also interested in using reconstruction techniques for human computer interaction. For his work, he received several awards, including the Otto Hahn Medal of the Max-Planck Society in 2007, the EUROGRAPHICS Young Researcher Award in 2009, the German Pattern Recognition Award 2012, the Karl Heinz Beckurts Award in 2017, and the EUROGRAPHICS Outstanding Technical Contributions Award in 2020. He is a Fellow of EUROGRAPHICS and of ELLIS (European Lab for Learning and Intelligent Systems). He received two ERC grants, an ERC Starting Grant in 2013 and an ERC Consolidator Grant in 2017.