Rate My Professor Justin Li

Li's research specializes in strategic knowledge search processes in humans and AI, including information seeking behavior, memory mechanisms within agent architectures, inference over semantic networks, and reinforcement learning applications to memory utilization. He examines how knowledge is retrieved at the appropriate time. Representative publications encompass journal works like "Multifaceted Equity: Critiquing a First-Year Writing Assessment through Curricular, Performance, and Reliability Lenses" with Julie Prebel (Journal of Writing Assessment, 2024), "Explorable Web Apps to Teach AI to Non-Majors" (The Journal of Computing Sciences in Colleges, 34(4), 2019), and "Towards Modeling False Memory with Computational Knowledge Bases" with Emma Kohanyi (Topics in Cognitive Science, 9(1), 2017). Key conference papers include "Exploring Integrated Co-occurrence and Semantic Mechanisms for Long Term Memory Retrieval" with Lily Gebhart (Proceedings of the 23rd International Conference on Cognitive Modeling, 2025), "Towards a Computational Model of a Dynamic Feeling of Knowing" (19th ICCM, 2022), "Integrating Declarative Long-Term Memory Retrievals into Reinforcement Learning" (8th Annual Conference on Advances in Cognitive Systems, 2020), and "Spontaneous Retrieval from Long-Term Memory in a Cognitive Architecture" with John E. Laird (29th AAAI Conference on Artificial Intelligence, 2015). Li has been honored with the Linda and Tod White Teaching Prize Finalist (Occidental College, 2024) and the Outstanding Graduate Student Instructor Award (University of Michigan EECS Department, 2009). As principal or co-principal investigator, he obtained grants such as the $493,878 NSF Major Research Instrumentation award for a high-performance computing cluster (2019), $1,000,000 NSF Scholarships in Science, Technology, Engineering, and Mathematics (2020), and $535,000 HHMI Inclusive Excellence 3 Initiative (2022).