Dr. LIU, Yang
Dr. LIU, Yang

劉泱博士
BEng, MEng, PhD
Assistant Professor, Department of Computer Science
https://www.comp.hkbu.edu.hk/~csygliu/
 

About

Dr. Liu is currently an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University and the Associate Director of the Health Informatics Center. He received his B.Eng. and M.Eng. degrees in Automation from National University of Defense Technology in 2004 and 2007, respectively. He received the Ph.D. degree in Computing from The Hong Kong Polytechnic University in 2011. During Feb.-Aug. 2010, he was a Visiting Scholar in the Robotics Institute at Carnegie Mellon University. Between 2011 and 2012, he was a Postdoctoral Research Associate in the Department of Statistics at Yale University. Dr. Liu's research interests include artificial intelligence, machine learning, applied mathematics, as well as their applications in high-dimensional/heterogeneous data analytics, computational epidemiology, and infectious disease modeling. He has published more than 80 peer-reviewed papers in reputable venues, including top-tier journals such as The Lancet Discovery Science, T-NNLS, T-Cyber, T-IP, T-AC, T-AMD, T-IST, PR, and NeuroImage, as well as top-tier conferences such as AAAI, IJCAI, SIGIR, and ACMMM.


Research Interests

  • Artificial Intelligence, Machine Learning, Pattern Recognition
  • Dimensionality Reduction, Subspace Learning
  • Multi-way/Multi-view/Multi-label/Multi-task Learning
  • Computational Epidemiology, Infectious Disease Modeling

Selected Publications

  • Jinfu Ren, Yang Liu, and Jiming Liu, Commonality and individuality based subspace learning, IEEE Transactions on Cybernetics (T-Cyber), In Press. 
  • Jinfu Ren, Mutong Liu, Yang Liu, and Jiming Liu, Optimal resource allocation with spatiotemporal transmission discovery for effective disease control, Infectious Diseases of Poverty, 11, Article number: 34, 2022.
  • Qi Tan, Yang Liu, and Jiming Liu, Demystifying Deep Learning in Predictive Spatiotemporal Analytics: An Information-Theoretic Framework, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 32(8), pp. 3538-3552, 2021.
  • Yang Liu, Zhonglei Gu, and Jiming Liu, Uncovering transmission patterns of COVID-19 outbreaks: A region-wide comprehensive retrospective study in Hong Kong, EClinicalMedicine, The LANCET Discovery Science, 36, 100929, June, 2021.
  • Yang Liu, Zhonglei Gu, Shang Xia, Benyun Shi, Xiao-Nong Zhou, Yong Shi, and Jiming Liu, What are the underlying transmission patterns of COVID-19 outbreak? – An age-specific social contact characterization, EClinicalMedicine, The LANCET Discovery Science, 22, 100354, May, 2020.
  • Tiantian He*, Yang Liu*, Tobey H. Ko, Keith C.C. Chan, and Yew-Soon Ong, Contextual Correlation Preserving Multi-View Featured Graph Clustering, IEEE Transactions on Cybernetics (T-Cyber), 50(10), pp. 4318-4331, 2020. (Joint First Author)
  • Yang Liu, Zhonglei Gu, Tobey H. Ko, and Jiming Liu, Identifying Key Opinion Leaders in Social Media via Modality-Consistent Harmonized Discriminant Embedding, IEEE Transactions on Cybernetics (T-Cyber), 50(2), pp.717–728, 2020.
  • Yang Liu, Tobey H. Ko, and Zhonglei Gu, Who is the Mr. Right for Your Brand? – Discovering Brand Key Assets via Multi-modal Asset-aware Projection, in Proceedings of 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-18), pp.1113–1116, 2018.
  • Kejing Yin, William K. Cheung, Yang Liu, Benjamin C. M. Fung, and Jonathan Poon, Joint Learning of Phenotypes and Diagnosis-Medication Correspondence via Hidden Interaction Tensor Factorization, in Proceedings of 27th International Joint Conference on Artificial Intelligence (IJCAI-18), pp.3627–3633, 2018.
  • Jiabei Zeng, Yang Liu, Biao Leng, Zhang Xiong, and Yiu-ming Cheung, Dimensionality Reduction in Multiple Ordinal Regression, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 29(9), pp.4088–4101, 2018.