David I. Inouye
Assistant Professor
Elmore Family School of Electrical and Computer Engineering (ECE)
Purdue University
BHEE 332 (EE 332)
Biography
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 the fundamentals of distribution alignment, probabilistic models, and explainable AI. More recently, I have focused on the fundamentals of distribution alignment including new algorithms, measures, and applications such as causality and domain generalization. On the explainable AI side, I am interested in explaining distribution shifts and tractable uncertainty quantification. 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).
Interests
- Deep distribution alignment
- Generative probabilistic models
- Explainable AI
- Invertible deep learning
- Iterative/Local learning
- Distribution shift
Education
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PostDoc in Machine Learning, 2019
Carnegie Mellon University
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PhD in Computer Science, 2017
The University of Texas at Austin
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MS in Computer Science, 2015
The University of Texas at Austin
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BS in Electrical Engineering, 2012
Georgia Institute of Technology
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BA in Natural Sciences, 2011
Covenant College
