Focus area: ML Systems

I am a Research Scientist at Prior Labs, where I develop the next generation of tabular foundation models. My work focuses on scalable pretraining, novel architectures, and scaling laws. I contribute to TabPFN-3, which scales tabular foundation models to datasets with up to one million training rows while substantially accelerating training and inference.

Previously, I was a Senior AI Researcher in Aleph Alpha Research’s Foundation Models team, working on efficient LLM pretraining, tokenizer-free architectures, and large-scale optimization. Before that, I was a Senior Expert at the Bosch Center for Artificial Intelligence, where my research centered on robust and reliable computer vision, neural architecture search, and synthetic data.

My broader interests span efficient and reliable machine learning, AutoML, and open-source software. I am an ELLIS member, an ELIZA Industrial Fellow, and an Area Chair for NeurIPS 2026. I have also contributed to scikit-learn, including its probability-calibration, kernel-ridge, and Gaussian-process modules.

I live in Böblingen and work in Freiburg. Outside research, I enjoy spending time with my wife and our three children. I am always happy to connect with people working on tabular foundation models, scalable pretraining, efficient or tokenizer-free architectures, and open-source machine learning.

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 in the German Cancer Research Center (DKFZ), working before as a junior data scientist in AstraZeneca Gothenburg (Sweden) and as Research Assistant in Uppsala University (Sweden). My current research is centered on model generalizability to different clinical settings, with a special focus on brain imaging, working to develop a stroke identification algorithm that works on a multi-centric setup. I am also part of a collaboration with the University of Amsterdam, where I am working with temporal brain image data to extract valuable patterns for model generalization. I have been awarded with several travel grants for international stays during my PhD studies, and excellence grants during my Bachelor and Master studies.

One of my most influencial publication is the following:

Martínez Mora, A., Subramanian, V., & Miljković, F. (2022). Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images. Journal of Computer-Aided Molecular Design, 36(6), 443-457.

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.