Suguman Bansal

My research interests lies in Formal Methods and Logic, and their applications to Artificial Intelligence, Machine Learning, and Programming Languages. The long-term research goal is to build Trustworthy AI by simplifying the design-development-deployment pipeline of safe and reliable AI systems.

My current research thrusts include:

• Formal Methods for Reinforcement Learning
• Reactive Synthesis from High-Level Specifications
• Symbolic Quantitative Reasoning

Previously, I was an NSF/CRA Computing Innovation Postdoctoral Fellow at the University of Pennsylvania, mentored by Rajeev Alur. I completed my Ph.D. at Rice University, advised by Moshe Y. Vardi and obtained my undergraduate degree from Chennai Mathematical Institute.

teaching

• CS 8803 (LCS): Logic in Computer Science — Fall 2026 (Upcoming), Fall 2023
• CS 8803 (FMR): Formal Methods in Reinforcement Learning — Fall 2025
• CS 4510: Automata and Complexity — Fall 2025, Spring 2024, Spring 2023

selected awards and honors

• CACM Article (Research and Advances) on Specification-Guided Reinforcement Learning
• Amazon Research Award (Fall 2024 Cycle)
• Best Paper Award, ATVA 2023
• Invited Tutorial Speaker at 28th Joint Conference on ETAPS 2025
• Keynote Speaker at 44th Conference on FSTTCS 2024
• Keynote Speaker 29th Static Analysis Symposium (SAS 2022)
• NSF/CRA Computing Innovation Fellow 2020
• MIT EECS Rising Star (2021, 2018)