Linglingzhi Zhu

Postdoctoral Fellow @ Georgia Institute of Technology

Office: ISyE Main 324
Email: llzzhu at gatech.edu or lzzhuling at gmail.com

[Google Scholar]

About Me

I am a Postdoctoral Fellow at the H. Milton Stewart School of Industrial and Systems Engineering (ISyE), Georgia Institute of Technology, working with Professor Yao Xie, and collaborating with Professor Xiuyuan Cheng at Duke University. I received my Ph.D. in Operations Research in 2024 from The Chinese University of Hong Kong (CUHK) under the supervision of Professor Anthony Man-Cho So. Before that, I received an M.S. in Computational Mathematics in 2020 and a B.S. in Mathematics in 2017 from Zhejiang University, where I was advised by Professor Chong Li. Additionally, I worked as a research assistant at The Hong Kong Polytechnic University from February to May 2019, under the mentorship of Professor Xiaoqi Yang.

Research Interests

Perturbation Theory, Geometric Optimization, Nonsmooth Analysis, Inverse Problems, Statistical Inference

Publications

  1. Shuffling Gradient Descent-Ascent with Variance Reduction for Nonconvex-Strongly Concave Smooth Minimax Problems [arXiv]
    Xia Jiang, Linglingzhi Zhu, Anthony Man-Cho So, Shisheng Cui, Jian Sun.
    preprint, 2024.

  2. Differentially Private Online Federated Learning with Correlated Noise [arXiv]
    Jiaojiao Zhang, Linglingzhi Zhu, Mikael Johansson.
    Accepted by 63rd IEEE Conference on Decision and Control (CDC 2024).

  3. Rotation Group Synchronization via Quotient Manifold [arXiv][slides]
    Linglingzhi Zhu, Chong Li, Anthony Man-Cho So.
    preprint, 2023.

  4. Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization [pdf]
    Taoli Zheng, Linglingzhi Zhu, Anthony Man-Cho So, José Blanchet, Jiajin Li.
    Advances in Neural Information Processing Systems 36 (NeurIPS 2023).

  5. Nonsmooth Nonconvex-Nonconcave Minimax Optimization: Primal-Dual Balancing and Iteration Complexity Analysis [arXiv]
    Jiajin Li, Linglingzhi Zhu, Anthony Man-Cho So.
    preprint, 2023.

  6. LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees [pdf]
    Shangyuan Liu, Linglingzhi Zhu, Anthony Man-Cho So.
    Advances in Neural Information Processing Systems 36 (NeurIPS 2023).

  7. Riemannian Linearized Proximal Algorithms for Nonnegative Inverse Eigenvalue Problem [pdf]
    (α-β) Sangho Kum, Chong Li, Jinhua Wang, Jen-Chih Yao, Linglingzhi Zhu.
    Numerical Algorithms (2023) 94:1819-1848.

  8. Linearized Proximal Algorithms with Adaptive Stepsizes for Convex Composite Optimization with Applications [pdf][slides]
    (α-β) Yaohua Hu, Chong Li, Jinhua Wang, Xiaoqi Yang, Linglingzhi Zhu.
    Applied Mathematics & Optimization (2023) 87:52.

  9. Nonsmooth Composite Nonconvex-Concave Minimax Optimization [pdf][slides]
    Jiajin Li, Linglingzhi Zhu, Anthony Man-Cho So.
    NeurIPS 2022 Workshop on Optimization for Machine Learning (OPT 2022), Oral.

  10. Orthogonal Group Synchronization with Incomplete Measurements: Error Bounds and Linear Convergence of the Generalized Power Method [arXiv][slides]
    Linglingzhi Zhu, Jinxin Wang, Anthony Man-Cho So.
    preprint, 2021.

Professional Service

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