Ting Kei Pong is a Professor in the Department of Applied Mathematics at the Hong Kong Polytechnic University, where he has been employed since August 1, 2014. His research focuses on continuous optimization, with particular expertise in convex relaxations and first-order methods for large-scale optimization problems. Dr. Pong received his Bachelor's degree in 2004 and MPhil degree in 2006 from the Department of Mathematics at the Chinese University of Hong Kong. He completed his PhD in 2011 from the Department of Mathematics at the University of Washington under the supervision of Professor Paul Tseng, with co-advisement from Professors Maryam Fazel and Rekha Thomas after Professor Tseng's disappearance. His postdoctoral training included positions at the University of Waterloo (2011-2013) and the University of British Columbia (2013-2014) as a PIMS postdoctoral fellow. His research interests span Continuous Optimization , with current focus on convex relaxations and first-order methods for large-scale problems. He also investigates constraint qualifications for convex optimization, statistical computation, and robust optimization. His work bridges theoretical foundations with practical applications in areas such as compressed sensing, sensor network localization, and machine learning. Dr. Pong's publication record demonstrates consistent high-impact contributions to optimization theory. His recent work (2022-2025) shows a strong focus on convergence analysis, error bounds for conic optimization problems, and development of efficient algorithms for nonconvex optimization. His research often combines theoretical analysis with practical implementation, as evidenced by the availability of code for many of his publications. He serves as Associate Editor for Mathematics of Operations Research (since 2019) and on the editorial boards of Computational Optimization and Applications, Pacific Journal of Optimization, and Open Journal of Mathematical Optimization. Dr. Pong actively mentors students and postdocs, currently supervising PhD students Yanbo Wang and Hao Zhang, and postdoc Jiefeng Xu. His former students have secured positions at institutions including the University of Texas Arlington, University of Hong Kong, and Sun Yat-Sen University. His advising reflects his research expertise, with students working on topics in nonconvex optimization, compressed sensing, and matrix factorization. He maintains an active research schedule, regularly presenting at major optimization conferences including ICCOPT, ISMP, and SIAM Optimization. His upcoming talks in 2025 demonstrate continued research productivity in error bounds for log-determinant cones and single-loop proximal-conditional gradient methods.
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CHEN, Yifanمشاهده پروفایل
استادیار
Yifan Chen is an Assistant Professor in the Department of Computer Science and affiliate faculty in the Department of Mathematics at Hong Kong Baptist University's Faculty of Science. He joined HKBU in Fall 2023 after completing his PhD in Statistics from the University of Illinois Urbana-Champaign in 2023 under the guidance of Prof. Yun Yang. His educational background includes: B.S. in Statistics from Fudan University (2018), advised by Prof. Juan Shen and Prof. Chenghong Zhang Ph.D. in Statistics from University of Illinois Urbana-Champaign (2023), advised by Prof. Yun Yang Dr. Chen's research focuses on developing efficient algorithms for machine learning, with particular emphasis on non-parametric models and neural networks featuring intensive matrix operations. His work bridges statistical theory with practical computational challenges in modern machine learning systems, especially those involving Transformers (language models) and Graph Neural Networks (GNNs). He approaches machine learning from both theoretical and applied perspectives, seeking to understand statistical structures while addressing real-world computational constraints. His publication record shows consistent output in top-tier venues including ICML, NeurIPS, KDD, and EMNLP, with recent work spanning graph coarsening, optimal transport, efficient language model fine-tuning, and causal inference. His research demonstrates strong mathematical foundations combined with practical applications in AI systems. Among his notable achievements: NSFC Young Scientists Fund (2025) GDSTC General Program funding (2024) RGC Early Career Scheme proposal grant (2024) ICML 2023 Grant Award ($1,500) Dr. Chen actively mentors students through his research group, supervising PhD students and visiting research assistants. He has successfully guided students who have gone on to PhD programs at institutions including Institute of Science Tokyo, HKU, Fudan, and NUS. His teaching includes COMP 7070 Advanced Topics in Artificial Intelligence and Machine Learning, which covers core machine learning concepts for AI application research, and COMP 2027 Applied Linear Algebra for Computing. His research group focuses on efficient machine learning algorithms, with current projects spanning graph neural networks, optimal transport, language model efficiency, and causal inference. He collaborates with researchers from institutions including UIUC, Fudan University, and industry labs like Amazon Alexa AI.