جستجوی استاد راهنمای دانشگاهی

Yang Liuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +۸ مورد دیگر

Dr. Yang Liu is an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University's Faculty of Science. He also serves as the Associate Director of the Health Informatics Center. His academic career spans prestigious institutions including Yale University and Carnegie Mellon University, demonstrating his expertise in both theoretical and applied aspects of computer science. Dr. Liu received his B.Eng. and M.Eng. degrees in Automation from National University of Defense Technology in 2004 and 2007, respectively. He earned his Ph.D. in Computing from The Hong Kong Polytechnic University in 2011. His academic journey included a Visiting Scholar position at Carnegie Mellon University's Robotics Institute (Feb.-Aug. 2010) and a Postdoctoral Research Associate position in the Department of Statistics at Yale University (2011-2012). Dr. Liu's research spans the intersection of artificial intelligence, machine learning, and practical applications in health and complex systems. His work focuses on artificial intelligence , machine learning , pattern recognition , dimensionality reduction , and subspace learning , with particular emphasis on multi-way/multi-view/multi-label/multi-task learning approaches. His research extends to modeling complex dynamical systems with applications in computational epidemiology and infectious disease modeling , addressing critical public health challenges through data-driven approaches. Analysis of Dr. Liu's recent publications reveals a strong focus on applying machine learning techniques to epidemiological challenges, particularly in modeling infectious disease transmission patterns. His work bridges theoretical advances in graph neural networks, subspace learning, and multi-view analysis with practical applications in public health. A significant portion of his research addresses the challenges of high-dimensional and heterogeneous data analytics, with applications ranging from malaria transmission modeling in Cambodia to uncovering COVID-19 transmission patterns in Hong Kong. Dr. Liu is an IEEE Senior Member and ACM Member, reflecting recognition of his contributions to the field. His paper "What are the underlying transmission patterns of COVID-19 outbreak? – An age-specific social contact characterization" was recognized as one of the most cited articles in EClinicalMedicine, Lancet Discovery Science, during 2020-2021, highlighting the impact of his work on pandemic response research. Dr. Liu actively mentors research students and regularly has research student and RA positions available. His professional service includes serving on editorial boards for SPJ Health Data Science and as a journal guest editor for special issues on cross-media analysis. He has extensive experience as a journal reviewer for top publications including IEEE TNNLS/TNN, IEEE T-Cyber/TSMC-B, IEEE TAC, IEEE TKDE, IEEE TMM, IEEE TCSVT, ACM TIST, ACM TOMM, and others, and serves on program committees for major conferences including WWW, IJCAI, and AAAI. Dr. Liu is affiliated with the Centre for Health Informatics and the Artificial Intelligence and Machine Learning Laboratory (AIML) at Hong Kong Baptist University. These research centers provide the infrastructure and collaborative environment necessary for his work in health informatics and machine learning applications. His role as Associate Director of the Health Informatics Center positions him at the forefront of interdisciplinary research connecting computing with public health challenges.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Deng Cai is a Professor at Zhejiang University's College of Computer Science, working in the State Key Laboratory of CAD&CG in Hangzhou, China. He also maintains an affiliation with Tencent AI Lab, demonstrating his strong connection between academic research and industry applications in artificial intelligence. His academic background includes a PhD from the University of Illinois at Urbana-Champaign, Department of Computer Science (2009). Professor Cai's research spans multiple domains within artificial intelligence, with particular emphasis on computer vision, deep learning, and their applications. His work shows strong focus on 3D object detection, lane detection for autonomous vehicles, and the application of large language models to various vision tasks. He has made significant contributions to traffic forecasting, trajectory prediction, and CAD generation systems. His recent work increasingly integrates large language models with computer vision tasks, demonstrating the evolving nature of his research interests toward multimodal AI systems. The trajectory of Professor Cai's publications reveals a clear progression from foundational computer vision and machine learning research toward increasingly complex and applied systems. His work shows strong emphasis on practical applications in autonomous driving, with numerous papers on 3D object detection, lane detection, and trajectory prediction. More recently, his research has expanded to include generative models for CAD systems and video customization, often leveraging large language models in innovative ways. The consistent publication output across top-tier venues including CVPR, ICCV, AAAI, and NeurIPS demonstrates sustained research productivity and impact. Professor Cai has established significant research collaborations, particularly with Xiaofei He (161 joint publications), Haifeng Liu (50), Zhou Zhao (42), Wenxiao Wang (41), and Binbin Lin (39). His work appears across diverse publication venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, and proceedings of major AI conferences. The breadth of his publication venues reflects the interdisciplinary nature of his research spanning theoretical machine learning to applied computer vision systems. Professor Cai leads research activities within Zhejiang University's College of Computer Science, particularly focusing on the State Key Laboratory of CAD&CG. His work bridges academic research with practical industry applications through his affiliation with Tencent AI Lab. The laboratory environment supports research in computer vision, machine learning, and their applications to real-world problems in autonomous systems, content generation, and intelligent transportation.