Siddharth Srivastava is an Associate Professor in the School of Computing and Augmented Intelligence at Arizona State University. Srivastava was a Staff Scientist at the United Technologies Research Center in Berkeley before joining ASU. Prior to that, he was a postdoctoral researcher working with Stuart Russell and Pieter Abbeel at the University of California Berkeley. Srivastava received his PhD in Computer Science from the University of Massachusetts Amherst, working with Shlomo Zilberstein and Neil Immerman, and a (4+1) MS in Mathematics from Indian Institute of Technology (IIT), Kanpur. Srivastava is a recipient of the NSF CAREER award, the Top 5% Faculty Award from the Fulton Schools of Engineering at ASU, a Best Paper Award at the International Conference on Automated Planning and Scheduling (ICAPS), an Outstanding Dissertation award from the Department of Computer Science at UMass Amherst, a Best Final Year Thesis award from the Department of Mathematics at IIT Kanpur and the National Board of Higher Mathematics Scholarship in India. He served as conference Co-Chair for ICAPS 2019. He currently serves as Chair of the ICAPS Awards Committee and as Associate Editor for the Journal of AI Research.
Srivastava's research focuses on developing principled, safe and reliable AI systems that can learn generalizable knowledge for planning and accomplishing complex user-desired tasks under uncertainty. His recent work develops the new research area of user-driven assessment of AI systems that can plan and learn, as well as algorithms for learning abstractions for long-horizon sequential decision making under uncertainty. His research interests include learning for sequential decision making systems in various settings including integrated task and motion planning, human-robot collaboration, generalized planning, hierarchical planning, Markov decision processes, and generalization and transfer in reinforcement learning. His work on planning for household robotics has been featured in international news media.