Refreshments are available starting at 10:30 a.m. The seminar will begin at 10:45 a.m.
Abstract
Causal knowledge is central to solving complex decision-making problems across engineering, medicine, and cyber-physical systems. Causal inference has been identified as a key capability to improve machine learning systems’ explainability, trustworthiness, and generalization. After a brief introduction to causal modeling, this talk explores two key problems in causal ML. In the first part of the talk, we will focus on the problem of root-cause analysis (RCA), which aims to identify the source of failure in large, modern computer systems. We will show that by leveraging ideas from causal discovery, it is possible to automate and efficiently solve the RCA problem by systematically using invariance tests on normal and anomalous data. In the second part of the talk, we consider causal inference problems in the presence of high dimensional variables, e.g., image data. We show how deep generative models, such as generative adversarial networks and diffusion models, can be used to obtain a representation of the causal system and help solve complex, high-dimensional causal inference problems. This approach enables both causal invariant prediction and evaluation of black box conditional generative models.
Speaker Biography
Murat Kocaoglu received his BS degree in electrical and electronics engineering with a minor in physics from the Middle East Technical University in 2010, his MS from the Koç University in Turkey in 2012, and his PhD from the University of Texas at Austin in 2018. Kocaoglu was a research staff member at the MIT-IBM Watson AI Lab at IBM Research in Cambridge, Massachusetts from 2018 to 2020. He is currently an assistant professor in the Elmore Family School of Electrical and Computer Engineering, the Department of Computer Science (by courtesy), and the Department of Statistics (by courtesy) at Purdue University, where he leads the CausalML Lab. Kocaoglu received an Adobe Data Science Research Award in 2022, an NSF CAREER Award in 2023, and an Amazon Research Award in 2024. His current research interests include causal inference, deep generative models, and information theory.