All members of our the IMPRS-IS community are invited to attend our sixth annual interview symposium taking place from Tuesday, January 18, 2022 to Friday, January 21, 2022. The event will feature two keynote presentations from IMPRS-IS faculty Dr. Moritz Hardt of the Max Planck Institute for Intelligent Systems and Dr. Michael Sedlmair of the University of Stuttgart.
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. Michael Sedlmair and Dr. Moritz Hardt.
Dr. Michael Sedlmair
Date: Wednesday, January 19, 2022
16:45 - 17:15
Interacting with AI
Abstract: In recent years, there has been a strong focus on fully autonomous AI technologies. While full automatization is easy to communicate in the media, the contemporary rhetoric seems to forget that most problems will continue to necessitate meaningful interactions between humans and machines. Along the life cycle of AI, there are many steps for which humans are needed in the loop: humans need to be able to understand the underlying data, explain AI models, or even cooperate with AI to excel at tasks such as decision making and music composition. Our work focuses exactly on these data, model, and AI interfaces. To that end, we develop and study techniques that combine approaches from human-computer interaction, data visualization, and mixed reality. In the talk, we will look at different examples of such human-data and human-AI interfaces, and think about when they are needed and how to properly build them.
Biography: Michael Sedlmair is a professor at the University of Stuttgart and leads the research group for Visualization and Virtual/Augmented Reality there. He received his Ph.D. degree in Computer Science from the University of Munich, Germany, in 2010. Further stops included the Jacobs University Bremen, University of Vienna, University of British Columbia in Vancouver, and the BMW Group Research and Technology, Munich. His research interests focus on visual and interactive machine learning, perceptual modeling for visualization, immersive analytics and situated visualization, novel interaction technologies, as well as the methodological and theoretical foundations underlying them.
Dr. Moritz Hardt
Date: Thursday, January, 20, 2022
16:45 - 17:15
Data, decisions, and dynamics
Abstract: Consequential decisions compel individuals to react in response to the specifics of the decision rule. This individual-level response in aggregate can disrupt the statistical patterns that motivated the decision rule, leading to unforeseen consequences.
In this talk, I will discuss two ways to formalize dynamic decision making problems. One, called performative prediction, directly makes assumptions about the aggregate population response to a decision rule. The other, called strategic classification, follows microeconomic tradition in modeling individuals as utility-maximizing agents with perfect information.
I will reflect on the advantages and limitations of either perspective, pointing out avenues for future research.
Based on collaborations with Anca Dragan, Meena Jagadeesan, Celestine Mendler-Dünner, John Miller, Smitha Milli, Juan Carlos Perdomo, Tijana Zrnic
Biography: Moritz Hardt is a director at the Max Planck Institute for Intelligent Systems. Prior to joining the institute, he was an Assistant Professor for Electrical Engineering and Computer Sciences at the University of California, Berkeley. Hardt's research contributes to the scientific foundations of algorithms and machine learning in social contexts. He co-authored the textbooks "Patterns, Predictions, and Actions: Foundations of Machine Learning" and "Fairness and Machine Learning: Limitations and Opportunities".
For access to these talks, please contact Sara Sorce (firstname.lastname@example.org).