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Hengjie Zhang

Affiliation - Huawei
zhanghengjie1@huawei.com

About Me

I am a full-time researcher/engineer at Huawei’s theory lab. Before joining Huawei, I completed my Ph.D. from Columbia University, where I was co-advised by Alexandr Andoni, Omri Weinstein, and Josh Alman.

I graduated from Yao Class at Tsinghua University in June 2019. In spring semester 2018, I visited University of Michigan and worked with Seth Pettie.

Selected Publications

  1. Sub-quadratic (1+eps)-approximate Euclidean Spanners, with Applications
    Alexandr Andoni, Hengjie Zhang
    64th Annual IEEE Symposium on Foundations of Computer Science, 2023.
    FOCS23

  2. Generalizations of Matrix Multiplication can solve the Light Bulb Problem
    Josh Alman, Hengjie Zhang
    64th Annual IEEE Symposium on Foundations of Computer Science, 2023.
    FOCS23

  3. Space-Efficient Interior Point Method, with applications to Linear Programming and Maximum Weight Bipartite Matching
    S. Cliff Liu, Zhao Song, Hengjie Zhang, Lichen Zhang, Tianyi Zhou
    2023 International Colloquium on Automata, Languages and Programming.
    ICALP23

  4. Faster Dynamic Matrix Inverse for Faster LPs
    Shunhua Jiang, Zhao Song, Omri Weinstein, Hengjie Zhang
    53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021.
    STOC21

  5. Near-optimal Distributed Triangle Enumeration via Expander Decompositions
    Yi-Jun Chang, Seth Pettie, Thatchaphol Saranurak, Hengjie Zhang
    Journal of the ACM, Volume 68, Issue 3, Article No.21, pp 1-36.
    JACM

The full list of my publications can be found here.


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