Liberating Movement Science from the Laboratory
Massive progress in machine learning technologies makes it easy and progressively precise to quantify movement in everyday situations. I will discuss how this enables the understanding of wider and wider ranges of behaviors in the real world. This development promises to make our research far more useful for the world. At the same time, our theories and ways of thinking need to adjust to a world in which complexity is beyond the grasp of us human scientists.
Dr. Konrad Kording runs his lab at the University of Pennsylvania. Konrad is interested in the question of how the brain solves the credit assignment problem and similarly how we should assign credit in the real world (through causality). In extension of this main thrust he is interested in applications of causality in biomedical research. Konrad has trained as student at ETH Zurich with Peter Konig, as postdoc at UCL London with Daniel Wolpert and at MIT with Josh Tenenbaum. After a decade at Northwestern University he is now PIK professor at UPenn.