Prof. May FUNG and Alumna Dr. WANG Yaqing Selected for AAAI New Faculty Highlights Program

Two members of the Department of Computer Science and Engineering have been selected for the 2026 New Faculty Highlights program of the Association for the Advancement of Artificial Intelligence (AAAI). They are Assistant Professor May FUNG and 2019 PhD graduate Dr. WANG Yaqing.

The AAAI New Faculty Highlights program recognizes early-career faculty members worldwide who have made significant contributions to AI research and show exceptional promise for future impact. This prestigious acknowledgment places them among the most promising new voices in the global AI research community.

Prof. May Fung’s research focuses on scaling trustworthy and human-centric foundation models through advanced reasoning and agentic AI frameworks. Her recent work proposes a unified paradigm of active multimodal knowledge grounding—where AI systems actively engage in deeper text-visual interleaved thinking; seek out, gather, and verify information from dynamic environments; and adapt their actions and resource use based on problem difficulty and user steering, rather than passively relying on static data or trained capabilities.

“Being selected for the AAAI New Faculty Highlights is a tremendous honor. It reflects not only the hard work of my wonderful research group and collaborators, but also the growing importance of building AI systems that are trustworthy, adaptable, and aligned with human needs. This recognition motivates me to continue pushing the boundaries of AI reasoning and human-AI collaboration, and I look forward to further exploring how these technologies can contribute to healthier and more intelligent information ecosystems,” said Prof. Fung.

Dr. Wang Yaqing is currently an Associate Professor at the Beijing Institute of Mathematical Sciences and Applications (BIMSA). She received her PhD in Computer Science and Engineering from HKUST in 2019, under the supervision of Prof. Lionel M. Ni and Prof. James T. Kwok. Her research focuses on machine learning and artificial intelligence, with an emphasis on data-efficient generalization, including few-shot learning, in-context learning, and adaptive agents.

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