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AI Research
AI Research in Computer Science
The William & Mary Computer Science Department conducts research across multiple areas of artificial intelligence. Our faculty and research labs contribute to advances in machine learning, natural language processing, computer vision, and AI applications. Through collaborative efforts and interdisciplinary partnerships, we work to develop innovative AI solutions and train the next generation of researchers in this important field.
Technical Pillars of AI Research @ CS
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AI Algorithms & Theory
We advance core learning methods (e.g., generalization, interpretable models, multi‑agent decision‑making, neural rendering). Faculty: Ashley Gao, Qun Li, Ayan Mukhopadhyay, Huajie Shao, Pieter Peers, Yanfu Zhang.
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Embodied AI
We build AI that senses and acts in the physical world (e.g., robotics, autonomous mobility, wearables, cyber‑physical systems) with rigorous safety. Faculty: Sidi Lu, Huajie Shao, Evgenia Smirni, Trey Woodlief, Gang Zhou.
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AI Systems & Hardware
We engineer the compute stack for AI (e.g., compilers/runtimes, GPU architecture, HPC/distributed, edge/vehicle platforms). Faculty: Qun Li, Bin Ren; Jie Ren; Yifan Sun.
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AI for Software Engineering
We build AI that understands, generates, tests, and secures code to raise software quality and developer productivity. Faculty: Oscar Chaparro, Antonio Mastropaolo, Denys Poshyvanyk, Yue Xiao.
AI With Purpose
Health & Wellbeing
We deploy AI to improve the quality of human life (e.g., wearable sensing, real‑time monitoring, decision support). Faculty: Ashley Gao, Huajie Shao, Janice Zhang, Gang Zhou.
Trustworthy, Secure & Responsible AI
We make AI reliable and safe at scale (e.g., privacy/compliance, robustness, safety‑critical autonomy, reliable ML systems). Faculty: Ashley Gao, Qun Li, Adwait Nadkarni, Evgenia Smirni, Huajie Shao, Trey Woodlief, Yue Xiao.
AI–Human Collaboration
We design human‑in‑the‑loop AI for developers, designers, and decision‑makers (e.g., code assistants, visualization, interactive ML). Faculty: Pieter Peers, Yifan Sun, Janice Zhang.
Education in the GenAI Era
We study AI literacy and design learning experiences and tools that help people learn with—and about—AI. Faculty: Peter Kemper, Huajie Shao, Denys Poshyvanyk, Yifan Sun, Janice Zhang.
Focus on High-Impact AI Research
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Award-Winning AI Research
Best Paper (e.g., KDD, CHASE), Most Influential Paper (e.g., MSR), and Test of Time awards (e.g., ASE) honors at top venues recognize breakthroughs in interpretable ML, health AI, and AI‑for‑SE.
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Real-World & Society Impact
Our models power deployments in clinics and communities (e.g., Parkinson’s monitoring, AI literacy), improving outcomes beyond the lab.
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Open Science
We release code, datasets, and evaluation protocols (e.g., GitHub, data portals) so others can adopt and independently verify our results.
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Student Leadership
Students co‑author top‑venue papers and collaborate with industry and federal labs on funded, production‑oriented projects.