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SUMMARY:Causal Machine Learning: Fundamentals and Applications - Prof Mura
 t Kocaoglu - Purdue University
DTSTART:20241122T150000Z
DTEND:20241122T160000Z
UID:TALK224005@talks.cam.ac.uk
CONTACT:Andreas Bedorf
DESCRIPTION:Causal knowledge is central to solving complex decision-making
  problems in many fields from engineering\, and medicine to cyber physical
  systems. Causal inference has also recently been identified as a key capa
 bility to remedy some of the issues modern machine learning systems suffer
  from\, from explainability and fairness to generalization. In this talk\,
  we first provide a short introduction to probabilistic causal inference. 
 Next\, we discuss how deep neural networks can be used to obtain a represe
 ntation of the causal system and help solve complex\, high-dimensional cau
 sal inference problems with deep generative models. We will also discuss s
 ome machine learning applications of the proposed algorithms.\n\nBio: Mura
 t Kocaoglu received his B.S. degree in Electrical - Electronics Engineerin
 g with a minor degree in Physics from the Middle East Technical University
  in 2010\, and M.S. degree from the Koc University\, Turkey in 2012 and Ph
 .D. degree from The University of Texas at Austin in 2018. He was a Resear
 ch Staff Member at the MIT-IBM Watson AI Lab in IBM Research\, Cambridge\,
  Massachusetts from 2018 to 2020. He is currently an assistant professor i
 n the Elmore Family School of Electrical and Computer Engineering\, Depart
 ment of Computer Science (by courtesy) and Department of Statistics (by co
 urtesy) at Purdue University where he leads the CausalML Lab. He received 
 the Adobe Data Science Research Award in 2022\, NSF CAREER Award in 2023\,
  and Amazon Research Award in 2024. His current research interests include
  causal inference\, deep generative models\, and information theory.
LOCATION:https://cam-ac-uk.zoom.us/j/84149251556?pwd=eB8d74qlkoDlHaJCjOroB
 rUIj2KFFb.1
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