Speaker: Seyed Jalal Etesami, Postdoctoral Researcher at the Computer Science Department and College of Management of Technology at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Title: Foundations of Causal Inference: Challenges and Opportunities
Abstract: Causal inference is a branch of machine learning and statistics that aims to develop theoretical models and practical algorithms to infer the statistical causal dynamics in complex systems. The incorporation of causality in learning is what predominantly sets human judgment apart from machines. Only when man-made systems acquire the ability to incorporate causal reasoning a true AI revolution will occur. In this talk, I will briefly explain causal transfer learning and Granger’s notion of causality in time series and discuss how his formulation can go beyond linear dynamics. As a special case of causal inference in times series, I will focus on efficient algorithms for learning the causal relationships in a network of multivariate Hawkes processes. Such processes are a type of temporal marked point processes which are widely used in a variety of applications, e.g., modeling earthquakes and analyzing epidemic pathways in global outbreaks of infectious diseases.
Bio: Jalal Etesami received his Ph.D. on the topic of causal inference from the University of Illinois at Urbana-Champaign (UIUC) in 2017. Afterward, he spent two years as a research scientist at Bosch center for artificial intelligence (BCAI), Germany, developing self-driving vehicles and modeling complex environments. He is currently a postdoctoral researcher at the Computer Science department and college of Management of Technology at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.
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