Konrad Kording, Ph.D.

Title: Machine learning for causality?

Abstract: Machine learning and causal inference are, arguably, the main techniques used to analyze medical data, but they tend to be seen as highly diverging ways of thinking about data.  Consequently, machine learning traditionally does not get at causality and causal inference research traditionally treats machine learning as a dangerous set of highly biased estimators. In my talk I will talk about our lab’s efforts to use machine learning as a component of more traditional quasi-experimental techniques. I will also discuss meta-learning approaches to causal inference, approaches where the estimators themselves are learned. I will focus on the opportunities that will arise by combing causal inference and machine learning.

Bio: Konrad Kording, Ph.D., is currently focusing on causality in data science applications - how do we know how things work if we cannot randomize? His early research focused on computational neuroscience, particularly on movement. He is known for his contributions to the fields of motor control, neural data methods, and computational neuroscience, as well as his advocacy for and contributions to open science and scientific rigor.

Professor Kording co-founded Neuromatch, a non-profit organization focused on promoting equity in the sciences and advancing open science policies. Some of his most controversial work involves predicting the future success of scientists, leading to the development of a calculator for predicting the h-index. Professor Konrad obtained both a diploma degree and a Ph.D. in physics from ETH Zurich. He then worked as a postdoctoral fellow at the Collegium Helveticum in Zurich and at University College London, followed by a Heisenberg Fellow position at MIT.

He joined the faculty at Northwestern University and the Rehabilitation Institute of Chicago, where he served as a professor of physical medicine, rehabilitation, physiology, and applied mathematics. In 2017, he joined the faculty at the University of Pennsylvania with joint appointments in the Department of Neuroscience and Department of Bioengineering