Rate My Professor Sumit Jha

Sumit Jha is a Professor in the Department of Computer & Information Science & Engineering at the University of Florida. He holds a Ph.D. in Computer Science from Carnegie Mellon University (2010), an M.S. in Computer Science from the same institution (2009), and a B.Tech. (Honors) in Computer Science and Engineering from the Indian Institute of Technology Kharagpur (2004). His primary research areas include artificial intelligence, machine learning, formal methods and logic, and emerging computing paradigms. Jha's work focuses on developing robust, interpretable, and controllable AI systems, as well as advancing formal methods for verification and emerging computing technologies.

Jha has a distinguished career marked by significant appointments, such as Eminent Scholar Chair Professor at Florida International University in 2024. He has earned prestigious awards including the FIU Top Scholar in 2025, Air Force Young Investigator Program Award in 2016, IEEE Orlando Section Outstanding Engineering Educator Award in 2013, and multiple best paper awards at conferences like FPS (2018), ICCABS (2014, 2011). His research has attracted substantial funding, including leading a $5 million U.S. Department of Energy consortium on MEDAL for autonomous scientific labs (2023-2025) and a $1 million DARPA project on robustness of machine learning systems (2020-2024). Jha's key publications appear in top venues such as ACL, CVPR, ICLR, ICML, AAAI, and DAC, with representative works including "NSF-CoT: Neuro-Symbolic Formal Verification of Chain-of-Thought Faithfulness in Contextual Question Answering" (ACL Findings, 2026), "Selective Amnesia using Contrastive Subnet Erasure for Class Level Unlearning in Vision Models" (CVPR, 2026), "Jailbreaking the Matrix: Nullspace Steering for Controlled Model Subversion" (ICLR, 2026), "Grammar-Forced Translation of Natural Language to Temporal Logic using LLMs" (ICML, 2025), and "LOGIC: Logic Synthesis for Digital In-Memory Computing" (ACM TODAES, 2025). His contributions have influenced AI safety, interpretability, and hardware-software co-design.