Qi Zhang

Biography

Qi Zhang is an assistant professor of the Computer Science and Engineering department and the Artificial Intelligence Institute at the University of South Carolina. He received his Ph.D. from the University of Michigan in 2021. His research aims for solutions for coordinating systems of decision-making agents operating in uncertain, dynamic environments. As hand-engineered solutions for such environments often fall short, he used ideas from planning and reinforcement learning to develop and analyze algorithms that autonomously coordinate agents in an effective, trustworthy, and communication-efficient manner. In particular, he has been working on social commitments for trustworthy coordination, communication learning, and language emergence among coordinated agents and applications of (multi-agent) reinforcement learning in intelligent transportation systems, dialogue systems, and multi-robot systems.

Talks and Events

2022 Tutorial: Knowledge-infused Reinforcement Learning

Virtual health agents (VHAs) have received considerable attention, but the early focus has been on collecting data, helping patients follow generic health guidelines, and providing reminders for clinical appointments. While presenting the collected data and frequency of visits to the clinician is useful, further context and personalization are needed for a VHA to interpret and understand what the data means in clinical terms. This has made their use in managing health limited. Such understanding enables patient empowerment and self-appraisal — i.e., aiding the patient in interpreting the data to understand the changes in the patient’s health conditions, and self-management — i.e., to help a patient better manage their health through better adherence to the clinician guidelines and clinician recommended care plan. Crisis conditions such as the current pandemic have further stressed our healthcare system and have made the need for such advanced support more attractive and in demand. Consider the rapid growth in mental health because the patients who already had mental health conditions worsen, and many develop such conditions due to the challenges arising from lockdown, isolation, and economic hardships. The severe lack of timely availability of clinical expertise to meet the rapidly growing demand provides the motivation for advancing this research in developing more advanced VHAs and evaluating it in the context of mental health management.