Siddharth Srivastava

Siddharth Srivastava is an Associate Professor in the School of Computing and Augmented Intelligence at Arizona State University. His 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 educational background includes:

• PhD in Computer Science from the University of Massachusetts Amherst, working with Shlomo Zilberstein and Neil Immerman
• MS in Mathematics from the Indian Institute of Technology (IIT), Kanpur

Dr. Srivastava's research interests span Artificial Intelligence, Robotics, AI Safety, and AI Assessment. He develops algorithms for generalizable planning and learning, with a focus on transfer learning, sequential decision making, and reinforcement learning. His work addresses the challenge of creating AI systems that can reliably (and safely) learn, generalize, and transfer knowledge to solve new tasks not encountered during training.

His notable scientific awards include:

• NSF CAREER award
• Top 5% Faculty Award from the Fulton Schools of Engineering at ASU
• Best Paper Award at the International Conference on Automated Planning and Scheduling (ICAPS)
• Outstanding Dissertation award from UMass Amherst
• Best Final Year Thesis award from IIT Kanpur
• National Board of Higher Mathematics Scholarship

Dr. Srivastava has advised several PhD students, including Pulkit Verma and Naman Shah (who recently defended their dissertations) and Rushang Karia. He leads the AAIR (Autonomous, Adaptive, and Intelligent Robotics) Lab at ASU, which focuses on developing principles and algorithms for reliable AI systems. The lab has organized workshops such as the GenPlan Workshop at NeurIPS and has been involved in significant events like the AAAI UR-RAD Symposium. His research has received press coverage from the BBC, Motherboard, AAAS Eureka Alert, NBC News, and IEEE Spectrum.