Yifan Chen

Yifan Chen is an Assistant Professor in Computer Science and Mathematics at Hong Kong Baptist University (HKBU), specializing in efficient machine learning with focus on non-parametric models and neural networks involving intensive matrix operations such as Transformers and Graph Neural Networks (GNNs).

• Affiliation: Department of Computer Science and Mathematics, Hong Kong Baptist University
• Location: Hong Kong SAR, China

Dr. Chen received his Ph.D. in Statistics from the University of Illinois Urbana-Champaign in 2023 under the supervision of Prof. Yun Yang, with additional collaborations with Prof. Ruoqing Zhu, Prof. Heng Ji, and Prof. Jingrui He. Prior to his doctoral studies, he earned his B.S. in Statistics from Fudan University in 2018, advised by Prof. Juan Shen and Prof. Chenghong Zhang.

Dr. Chen's research broadly focuses on understanding the statistical structures of modern machine learning algorithms and applying these insights to real-world computational challenges. His work particularly emphasizes non-parametric models and neural networks with intensive matrix operations, including Transformers (language models) and graph neural networks. His research bridges theoretical statistical learning with practical computational efficiency, exploring how to make machine learning algorithms both theoretically sound and computationally feasible for large-scale applications.

Analysis of Dr. Chen's publication record reveals a strong focus on computational efficiency in machine learning, with recurring themes in graph-based learning, kernel methods, and optimization techniques. His work demonstrates expertise in both theoretical foundations and practical implementations, with publications spanning top-tier conferences including NeurIPS, ICML, KDD, and AAAI. Many of his papers address fundamental challenges in scaling machine learning algorithms while maintaining statistical guarantees, reflecting his commitment to both theoretical rigor and practical applicability.

Scientific awards and recognition include:

• Dissertation Completion Fellowship from University of Illinois Graduate College (2023)
• ICML 2023 Grant Award (2023)
• Shanghai Outstanding Graduate from Shanghai Municipal Education Commission (2018)
• NSFC Young Scientists Fund (2025)
• GDSTC General Program funding (2024)
• RGC Early Career Scheme grant (2024)

Dr. Chen actively mentors PhD students and research assistants, currently supervising Yifan Xu and Yujia Yin as PhD students, and Yifan Wu as a visiting research assistant. His group has produced notable alumni including Mingchen Jiang (now PhD student at Institute of Science Tokyo), Jiahao Ma (now PhD student at HKU), Xichen Ye (now incoming PhD student at Fudan), and Guoming Li (now incoming PhD student at NUS). He teaches advanced topics in artificial intelligence and machine learning, as well as applied linear algebra for computing, emphasizing the connections between theoretical foundations and practical applications.

Dr. Chen maintains active collaborations with researchers at institutions including University of Illinois Urbana-Champaign, Fudan University, Shanghai University, and industry partners like Amazon Alexa AI and IBM Research.