جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Non-parametric Methods
• Computational Methods
• +۶ مورد دیگر

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.

Jingcun Caoمشاهده پروفایل

استادیار

• Mobile App Ecosystem
• Online Education
• Health Care
• +۹ مورد دیگر

Jingcun Cao is an Assistant Professor at the University of Hong Kong, where he joined in 2020. He holds a Ph.D. in Marketing from Indiana University, an M.A. in Economics and Business from the same institution, and a B.S. in Computational Mathematics from Xiamen University. He also completed visiting programs at the University of Chicago Booth School of Business and National Tsing Hua University. His research addresses mobile app ecosystems, online education, healthcare, digital marketing, and environmental policy. Methodologically, he specializes in causal inference, applied machine learning, randomized field experiments, and econometrics. His collaborations with tech firms focus on business intelligence and big data analytics. Teaching responsibilities include courses such as Big Data Marketing (MKTG 3530), Introduction to Marketing (MKTG 2501), and executive education modules in MarTech, Business Analytics, and Brand Management. His publications analyze trends in digital platform governance, deep learning applications for consumer behavior, socioeconomic impacts of policy interventions, and mobile app monetization strategies. Research consistently integrates multidisciplinary approaches to solve industry-relevant problems. He is currently recruiting Research Assistants and holds pending patents for machine-learning algorithms related to marketing analytics.

• Control Theory
• Estimation and filtering theory
• Complex dynamical distributed networks
• +۳ مورد دیگر

Daniel W. C. HO is a Chair Professor of Applied Mathematics and Associate Dean (Undergraduate Education) at the College of Science, City University of Hong Kong. He has been with City University of Hong Kong since 1989, having previously served as a Research Fellow at the University of Strathclyde, Glasgow, UK from 1985 to 1988. Prof. Ho received first class honours in BSc, MSc, and PhD degrees in mathematics from the University of Salford, Greater Manchester, UK in 1980, 1982, and 1986, respectively. His academic journey began with foundational work in control theory and has evolved into a distinguished career spanning over three decades. Prof. Ho's research interests span multiple domains in control theory and systems engineering. His primary focus areas include Control Theory , Estimation and filtering theory , Complex dynamical distributed networks , Multi-agent networks , Nonlinear singular systems , and Stochastic systems . His work bridges theoretical advances with practical applications, particularly in networked control systems, cybersecurity for cyber-physical systems, and distributed optimization. Prof. Ho has made significant contributions to the understanding of synchronization phenomena in complex networks, resilient control under cyber attacks, and quantized control systems with communication constraints. His research has evolved from classical control theory to address contemporary challenges in networked and distributed systems, reflecting the changing landscape of control engineering. Prof. Ho's publication record shows a strong emphasis on secure control systems under cyber attacks, distributed optimization with communication constraints, event-triggered control schemes, quantized control systems, and synchronization of complex networks. His work demonstrates a consistent progression from theoretical foundations to addressing practical implementation challenges in cyber-physical systems, with increasing focus on security aspects in recent years. Prof. Ho has received numerous prestigious awards and honors throughout his career. He was named a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2017 and elevated to IEEE Life Fellow status in 2024. He was awarded the Chang Jiang Chair Professorship by the Ministry of Education, China in 2012. Prof. Ho has been recognized as a Highly Cited Researcher for eleven consecutive years from 2014 to 2024, and is among the Top 2% of most highly cited scientists globally from 2020 to 2024. He received the Best Paper Award from The 8th Asian Control Conference in 2011 and the Teaching Excellence Award from City University of Hong Kong in 2020 for his innovative teaching approaches. Prof. Ho has held significant editorial responsibilities, serving as Subject Editor of the Journal of Franklin Institute, Co-Editor in Chief of Franklin Open, Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, Asian Journal of Control, and Action Editor of Neural Networks. He has also served on the editorial boards of several other prestigious journals, contributing to the advancement of his field through scholarly communication. His leadership extends beyond research and teaching as Associate Dean (Undergraduate Education) of the College of Science at City University of Hong Kong, where he plays a key role in shaping the educational experience for science students.

SONG Junمشاهده پروفایل

استادیار

• Artificial Intelligence
• Geographic Information Systems
• Environmental Science
• +۵ مورد دیگر

Dr. SONG Jun serves as a Research Assistant Professor in the Department of Geography at Hong Kong Baptist University and is an active member of the Smart Society Lab. His work bridges artificial intelligence with geographical and environmental challenges, focusing on practical urban sustainability solutions. His academic credentials include: Ph.D. from Imperial College London (2017-2021) M.Sc. and B.Sc. from Southwest Jiaotong University, China (2010-2016) Dr. SONG pioneers AI-driven geographical analysis , environmental monitoring systems , and smart city infrastructure . His research develops machine learning frameworks for air pollution mapping, methane monitoring optimization, and pandemic exposure reduction through IoT networks. Key methodologies integrate deep learning with spatiotemporal data to address climate resilience in Chinese urban contexts. His 2021-2023 publications reveal a strong trajectory in applying computer vision to transportation systems and neural networks to environmental sensing. The work spans climate science, public health, and urban informatics, with notable emphasis on carbon management and pandemic response infrastructure in China. Major recognitions include: China Youth Entrepreneurship Award (2017) Cloud-Guizhou Young Scientist Award (2018) AI Challenger Award (2018) As Associate Editor for IEEE Transactions on Emerging Topics in Computational Intelligence, Dr. SONG contributes to academic discourse while serving on the Beijing-Tianjin-Hebei Big Data Association's Environment Protection Committee and the Yangtze River Delta Carbon Neutrality Strategic Development Committee. His Smart Society Lab initiatives focus on real-world AI deployment for sustainable urban development.