IMPRS-IS 2023 Interview Symposium Keynotes (Symposium)

The International Max Planck Research School for Intelligent Systems (IMPRS-IS) brings together the MPI for Intelligent Systems with the University of Stuttgart and the University of Tübingen to form a highly visible and unique graduate school of internationally recognized faculty, working at the leading edge of the field. This program is a key element of Baden-Württemberg’s Cyber Valley initiative to accelerate basic research and commercial development in artificial intelligence.

Each year in January, we host a symposium to interview IMPRS-IS Ph.D. applicants. The four-day event will also feature keynote talks by our faculty members Dr. Daniel Häufle and Dr. Mathias Niepert.
 
Dr. Daniel Häufle
Date: Wednesday, January 18, 2023
16:45 - 17:30

Morphological computation in neuro-muscular control of movement

Abstract: Currently to me, the biggest thrill in robotics is to see that robots learn to interact with the real world. Robots are now able to walk in uncertain environments and deal with perturbations. While this is exciting to study and to see, I would claim that biology solved these problems millions of years ago. Surprisingly, we still do not fully understand the principles behind the biological solution. I have the great pleasure to collaborate with fantastic colleagues in the IMPRS-IS. Together, we try to better understand the interaction between neuronal circuits, musculoskeletal dynamics, and the environment. In my talk, I will present past and ongoing research within IMPRS-IS. I will show why and how we develop computer simulations of neuro-muscular control and learning, why and how we translate them into robotic concepts, and why I believe that this is relevant for robotic assistance in neuro-rehabilitation.

Biography: Daniel Häufle is assistant professor and head of the Research Group Multi-Level Modeling in Motor Control and Rehabilitation Robotics at Hertie Institute for Clinical Brain Research, University of Tübingen, and the Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart. His group investigates how muscles and the nervous system interact to generate human movement and how this interaction may be impaired in neurological movement disorders. He studied physics and biomechanics in Jena, Germany and Calgary, Canada. For his PhD, he worked in the Computational Biopyhsics & Biorobotics Lab of Syn Schmitt at the University of Stuttgart. With a Fulbright Scholarship he visited the Robotics Institute of Carnegie Mellon University in Pittsburgh, USA. His habilitation in Computer Science at the University of Tübingen focused on the contribution of morphology to the control of biological movement.
 
Dr. Mathias Niepert
Date: Thursday, January 19, 2023
16:45 - 17:30

Learning with discrete structures and algorithms

Abstract: Machine learning at scale has led to impressive results ranging from text-based image generation, reasoning with natural language, and code synthesis to name but a few.  ML at scale is also  successfully applied to a broad range of problems in engineering and the sciences. These recent developments make some of us question the utility of  incorporating prior knowledge in form of symbolic (discrete) structures and algorithms. Is computing and data at scale all we need?

We will make an argument that discrete (symbolic) structures and algorithms in machine learning models are  advantageous and even required in numerous application domains such as Biology, the Material Sciences, and Physics. Biomedical entities and their structural properties, for example, can be represented as graphs and require inductive biases equivariant to certain group operations. My labs research is concerned with the development of machine learning methods that combine discrete structures with continuous equivariant representations. We also address the problem of learning and leveraging structure from data where it is missing, combining discrete algorithms and probabilistic models with gradient-based learning.  We will show that discrete structures and algorithms appear in numerous places such as ML-based PDE solvers and that modeling them explicitly is indeed beneficial. Especially machine learning models with the aim to exhibit some form of explanatory properties have to rely on symbolic representations. The talk will also cover some biomedical and physics related applications.

Biography: Mathias Niepert is a professor at the University of Stuttgart and a faculty member of the International Max Planck Research School for Intelligent Systems (IMPRS-IS). He heads the Machine Learning and Simulation Lab. His professorship is part of the Cluster of Excellence for the Simulation Sciences (SimTech), the Department of Computer Science, and the ELLIS society. He is also a Chief Scientific Advisor at NEC Laboratories Europe. At NEC Labs Europe he was senior (2015-2017) and chief research scientist (2017-2021) as well as manager (2019-2021) of the machine learning group. From 2013-2015 he was a postdoctoral research associate at the Allen School of Computer Science, University of Washington, Seattle. Dr. Niepert obtained his PhD from Indiana University. His group's research interests include representation learning for discrete structures and algorithms, geometric deep learning, probabilistic graphical models, and the intersection of ML and the sciences.

For access to these talks, please contact IMPRS-IS Communications Manager Sara Sorce (imprs@is.mpg.de).