David I. InouyeView profile
Assistant Professor
David I. Inouye is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering (ECE) at Purdue University. His research focuses on trustworthy AI/ML, causal inference, distribution robustness, and explainable AI. He holds a PhD in Computer Science from The University of Texas at Austin and completed a postdoc at Carnegie Mellon University. Education: 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 Research Interests: Developing robust machine learning methods resilient to distribution shifts and computational assumptions Exploring causal mechanisms to mitigate ML robustness issues Advancing explainable AI and fairness in automated decision systems Designing robust collaborative learning frameworks for edge device networks Publications Highlight Trends in: Counterfactual fairness and causal reasoning Federated learning and domain generalization Generative models and distribution matching Vertical data partitioning and dynamic network inference Grants: Funded by NSF, Army Research Laboratory (ARL), and Office of Naval Research (ONR). Active lab collaborations include projects on federated domain translation and causal ML. Labs/Teams: Leads the Inouye Lab, with contributions to open-source tools like FedINB and StarCraftImage datasets.
