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Qiujiang Jin

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Graduated Ph.D. student
Department of Electrical and Computer Engineering
Wireless Networking and Communications Group
University of Texas at Austin
E-mail: qiujiangjin0@gmail.com

About me

I graduated with my Ph.D. degree from the Department of Electrical and Computer Engineering of University of Texas at Austin. I was a member of the Wireless Networking and Communications Group and I was advised by Prof. Aryan Mokhtari. I received my B.Sc. in pure and applied mathematics from the Department of Mathematical Sciences at Tsinghua University and my M.Sc. in computational and mathematical engineering from the Institute for Computational & Mathematical Engineering at Stanford University. My research interests generally lie in the theory and applications of optimization. My current research focuses on the development of efficient second-order numerical optimization methods for large-scale machine learning problems.

Publications and Preprints

  1. [Non-asymptotic Superlinear Convergence of Standard Quasi-Newton Methods]
    Qiujiang Jin and Aryan Mokhtari
    Mathematical Programming, 1-49, 2022.

  2. [Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach]
    Qiujiang Jin and Aryan Mokhtari
    Advances in Neural Information Processing Systems (NeurIPS), 2021.

  3. [Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood]
    Qiujiang Jin, Alec Koppel, Ketan Rajawat and Aryan Mokhtari
    International Conference on Machine Learning (ICML), 2022.

  4. [Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence]
    Ruichen Jiang, Qiujiang Jin and Aryan Mokhtari
    Conference on Learning Theory (COLT), 2023.

  5. [Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models]
    Qiujiang Jin, Tongzheng Ren, Nhat Ho and Aryan Mokhtari
    Transactions on Machine Learning Research, 2024.

  6. [Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search]
    Qiujiang Jin, Ruichen Jiang and Aryan Mokhtari
    Advances in Neural Information Processing Systems (NeurIPS), 2024. (Spotlight)

  7. [Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization]
    Ruichen Jiang, Ali Kavis, Qiujiang Jin, Sujay Sanghavi and Aryan Mokhtari
    Advances in Neural Information Processing Systems (NeurIPS), 2024.

  8. [Non-asymptotic Global Convergence Rates of BFGS with Exact Line Search]
    Qiujiang Jin, Ruichen Jiang and Aryan Mokhtari
    Mathematical Programming, 2025.

  9. [Affine-Invariant Global Non-Asymptotic Convergence Analysis of BFGS under Self-Concordance]
    Qiujiang Jin and Aryan Mokhtari
    Under Review, 2025