Siddharth Srivastava

Siddharth Srivastava is an Associate Professor at Arizona State University whose research focuses on artificial intelligence, automated planning, and robotics, with particular emphasis on long-horizon decision making under uncertainty. His work investigates how symbolic reasoning, hierarchical planning, and learning-based methods can be combined to enable robots and AI systems to operate robustly in complex, real-world environments. A central theme of his research is task and motion planning, including the development of abstractions and option-invention techniques that allow agents to generalize across tasks and settings. He has also made significant contributions to explainable AI and AI assessment, exploring how to evaluate the capabilities, limitations, and reasoning behavior of black-box decision-making systems, including large language models. His research appears in leading venues such as ICLR, NeurIPS, AAAI, ICAPS, CoRL, and the Journal of Artificial Intelligence Research, reflecting a strong commitment to principled, interpretable, and reliable AI for autonomous systems.