Jie Wang



Laboratory of Machine Intelligence Research and Applications (MIRA)
Department of Electronic Engineering and Information Science (EEIS)
University of Science and Technology of China



About Me

Jie Wang is a Professor at University of Science and Technology of China (USTC). He received his BS in electronic information science and technology from USTC in 2005 and his PhD in computational science in 2011 from the Florida State University. Then, he went on to conduct his postdoctoral work at Arizona State University followed by the University of Michigan. Before joining USTC, Dr. Wang held a position of research assistant professor at University of Michigan from 2015. He has broad interests in artificial intelligence, machine learning, data mining, natural language processing, image processing, and large-scale optimization etc. He has published many papers on top machine learning and data mining journals and conferences such as JMLR, TPAMI, NIPS, ICML, and KDD. He is the PI of research projects funded by the Thousand Talent Program for Young Outstanding Scientists (青年千人) and National Science Fund for Excellent Young Scholars(国家优青).


I am recruiting strongly self motivated talents — including undergraduates, graduates, and postdocs — to join my group. Candidates are expected to have strong mathematical and/or programming skills. Interested parties should contact Jie Wang with the following documentation:

  • a current CV,

  • undergraduate and/or graduate transcripts,

  • a representative publication if applicable.

  • Our screening method, DPP, is highlighted by leading researchers in their new book (Section 5.10):
    Statistical Learning with Sparsity: The Lasso and Generalizations.
    Trevor Hastie, Robert Tibshirani, and Martin Wainwright

  • Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning.
    Tingjin Luo, Weizhong Zhang, Shuang Qiu, Yang Yang, Dongyun Yi, Guangtao Wang, Jieping Ye, and Jie Wang.
    SIGKDD 2017.

  • The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands on Large-Scale Online.
    Yongxin Tong, Yuqiang Chen, Zimu Zhou, Lei Chen, Jie Wang, Qiang Yang, and Jieping Ye.
    SIGKDD 2017.

  • Scaling Up Sparse Support Vector Machine by Simultaneous Feature and Sample Reduction.
    Weizhong Zhang, Bin Hong, Jieping Ye, Deng Cai, Xiaofei He, and Jie Wang.
    ICML 2017. [Code Download]