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About

Justin Lidard is a researcher at the forefront of trustworthy AI, focusing on the intersection of uncertainty quantification, large language models (LLMs), and human-robot interaction. His major contributions include pioneering a comprehensive taxonomy for uncertainty quantification in LLMs, addressing critical challenges in reliability and trustworthiness as these models are increasingly deployed in content generation, coding, and reasoning tasks. His highly cited survey on this topic (26 citations) has become a foundational reference for researchers seeking to understand and mitigate the risks of LLM integration. Lidard also advances safe human-robot collaboration through his work on risk-calibrated interaction, where he developed set-valued intent prediction methods that enable robots to operate with calibrated confidence, reducing the potential for harmful outcomes. His research bridges theoretical frameworks with practical safety mechanisms, making him a notable voice in the push toward dependable AI systems. With a growing citation impact and a focus on open research challenges, Lidard’s work is essential reading for anyone interested in building AI that is both powerful and trustworthy.