معرفی
Jing Yuan serves as an Assistant Professor in the Department of Computer Science and Engineering at the University of North Texas, where he directs the Robust Adaptive Learning (RAL) Lab. His research centers on theoretical foundations of artificial intelligence and machine learning, specifically developing advanced optimization techniques to enhance algorithmic performance and efficiency for building robust, adaptable AI systems capable of solving complex real-world problems across diverse domains.
Dr. Yuan's primary research focuses on submodular optimization, adaptive algorithms, and fairness in AI systems. He investigates approximation algorithms for sequential decision-making under uncertainty, calibrated recommendations, and non-monotone optimization problems. His work bridges theoretical computer science with practical applications in recommender systems, social advertising, and personalized learning, emphasizing algorithmic efficiency while addressing societal concerns like equity and ethical constraints in AI deployment.
Analysis of his recent publications reveals a concentrated research trajectory in adaptive submodular maximization and its extensions. The body of work demonstrates rigorous theoretical contributions in approximation guarantees while tackling practical challenges including fairness constraints, sponsored content integration, and multi-objective optimization in dynamic environments. This research has been consistently published in premier AI conferences such as AAAI, IJCAI, and AAMAS, reflecting significant impact in both theoretical and applied communities.
Dr. Yuan actively mentors PhD students including Twumasi, whose research on incentivized social advertising was accepted to AAAI ICWSM 2024. He has successfully recruited and guided numerous undergraduate researchers through programs like LREU and Google CAHSI, with multiple students presenting at conferences including GMiS and University Research Days. His research is supported by a Google CAHSI Institutional Research grant for fairness-aware optimization, and he recruits new PhD students annually with full financial support.
The Robust Adaptive Learning Lab at UNT has demonstrated steady growth since 2022, currently comprising multiple PhD students and a rotating cohort of undergraduate researchers. The lab maintains active collaborations with industry partners like Google and participates in the CAHSI network, focusing on developing theoretically sound AI solutions with tangible societal impact through rigorous algorithmic design and empirical validation.
