人员简介

李文烨

职务/职称：

Research Scientist

研究方向：

Artificial Intelligence, Machine Learning

电子邮箱：

wyli@cuhk.edu.cn

教育背景：

PhD (The Chinese University of Hong Kong)

MS (Chinese Academy of Sciences, China)

BS (Shandong University, China)

主要成果/荣誉：

  1. Presidential Exemplary Teaching Award, The Chinese University of Hong Kong, Shenzhen, 2022.
  2. Best Paper Finalist (short paper), Learning Sparse Binary Code for Maximum Inner Product Search, ACM-CIKM’2021.
  3. Best Paper Finalist, Wireless Sensor Network Cluster Locations: A Probabilistic Inference Approach, IEEE-ICAL’2011.
  4. Best Paper in Logistics Award, Facility Locations Revisited: An Efficient Belief Propagation Approach, IEEE-ICAL’2010.
  5. Student Research Award, Generalized Regularized Learning, IEEE CIS HK Chapter, 2007.

个人介绍：

Prof. Li received B.S. degree from Shandong University (1995-1999), M.S. degree from Chinese Academy of Sciences (1999-2003), and Ph.D degree from The Chinese University of Hong Kong (2003-2007), all in computer science. He carried out postdoc research at The Chinese University of Hong Kong and University of Alberta from 2007 to 2009. After that, he was with Macao Polytechnic Institute as a faculty member from 2009 to 2016. In Aug. 2016, he joined The Chinese University of Hong Kong (Shenzhen) and Shenzhen Research Institute of Big Data (SRIBD).

Prof. Li’s research focuses on machine learning and artificial intelligence, and has published over 40 first-authored papers. As the principal investigator, he has completed six projects of fundamental research. He has served as PC/SPC/AC members for NIPS/NeurIPS, ICML, IJCAI, AAAI, and many other academic conferences.

代表性论文：

  1. Li Wenye, Yu F., Ma Z. (2023) Metric Nearness Made Practical. To appear in The 37th AAAI Conference on Artificial Intelligence (AAAI’2023).
  2. Li Wenye, Yu F. (2022) Calibrating Distance Metrics Under Uncertainty. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2022 (ECML’2022).
  3. Li Wenye (2020) Modeling Winner-Take-All Competition in Sparse Binary Projections. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020 (ECML’2020).
  4. Li Wenye, Mao J., Zhang Y., Cui S. (2018) Fast Similarity Search via Optimal Sparse Lifting. In Advances in Neural Information Processing Systems 31 (NeurIPS’2018).
  5. Li Wenye, Zhang J., Zhou J., Cui L. (2018) Learning Word Vectors with Linear Constraints: A Matrix Factorization Approach. In 27th International Joint Conference on Artificial Intelligence (IJCAI’2018).