About me

I am a second-year Ph.D. student in the Department of Computer Science and Engineering at the State University of New York at Buffalo, where I am fortunate to be advised by Prof. Kaiyi Ji. Prior to this, I earned my M.S. in Statistics from the University of Illinois at Urbana-Champaign in 2022 and my B.S. in Mathematics from Central South University (China) in 2020.


Research

I have been working at the intersection of optimization, machine learning and networked systems, mostly on the theory side. My major research focuses include:
  • Bilevel optimization
  • Adaptive optimization
  • Federated learning and communication networks
  • Large-scale stochastic optimization

Publications

  • Tuning-Free Bilevel Optimization: New Algorithms and Convergence Analysis.

    •          Yifan Yang, Hao Ban, Minhui Huang, Shiqian Ma, Kaiyi Ji.
             [Preprint]

  • First-Order Federated Bilevel Learning.

    •          Yifan Yang, Peiyao Xiao, Shiqian Ma, Kaiyi Ji.
             [AAAI 2025]

  • First-Order Minimax Bilevel Optimization.

    •          Yifan Yang*, Zhaofeng Si* , Siwei Lyu, Kaiyi Ji.
             [NeurIPS 2024]

  • SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning.

    •          Yifan Yang, Peiyao Xiao, Kaiyi Ji.
             [NeurIPS 2023 (Spotlight, 3% Acceptance)]

  • Achieving O(\epsilon^{-1.5}) Complexity in Hessian-free Stochastic Bilevel Optimization.

    •          Yifan Yang, Peiyao Xiao, Kaiyi Ji.
             [NeurIPS 2023]

  • Imperative Learning: A Self-supervised Neural-Symbolic Learning Framework for Robot Autonomy.

    •          Chen Wang, Kaiyi Ji, Junyi Geng, Zhongqiang Ren, ..., Yifan Yang, Xiao Lin, Zhipeng Zhao.
             [Preprint]

    Talks

    Glad to give an invited talk at INFORMS 2024 (Rice University, Houston, TX) about our recent progress on bilevel optimization. Many thanks to conference organizer Prof. Shiqian Ma and session organizer Dr. Jeongyeol Kwon.

    Service

    I served as a conference reviewer of:
  • ICML 2025
  • ICLR 2024, 2025
  • NeurIPS 2024
  • AISTATS 2025
  • ACML 2024
    • I served as a journal reviewer of:
  • Journal of Machine Learning Research
  • IEEE Transactions on Signal Processing
  • SIAM Journal of Optimization
  • Fractal and Fract
  • IEEE Transactions on Circuits and Systems for Video Technology

    • Teaching Experiences

    I worked as a teaching assistant of the following courses:
  • CSE676: Deep Learning (2024 Spring)
  • CSE431/531: Algorithm Analysis and Design (2023 Fall)
  • CSE460/560: Data Models and Query Languages (2023 Spring)