I just started using Twitter for announcements. If you’re interested in distribution alignment, localized learning, or explainable AI, please check out my Twitter @davidinouye.
I am an assistant professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. I lead the Probabilistic and Understandable Machine Learning Lab. My research interests are in machine learning focused on distribution alignment (slides, video), localized learning, and explainable AI. Previously, I was a postdoc at Carnegie Mellon University working with Prof. Pradeep Ravikumar. I completed my Computer Science PhD at The University of Texas at Austin in 2017 advised by Prof. Inderjit Dhillon and Prof. Pradeep Ravikumar. I was awarded the NSF Graduate Research Fellowship (NSF GRFP).
PostDoc in Machine Learning, 2019
Carnegie Mellon University
PhD in Computer Science, 2017
The University of Texas at Austin
MS in Computer Science, 2015
The University of Texas at Austin
BS in Electrical Engineering, 2012
Georgia Institute of Technology
BA in Natural Sciences, 2011
Covenant College
Distribution alignment has the opposite objective of classification and is useful for constraining ML problems.
Localized learning avoids end-to-end learning by updating model parts via non-global objectives.
Explaining distribution shifts and prediction uncertainty are important for modern AI systems.