معرفی
Siqi Zhang is a Tenure-Track Assistant Professor in the Department of Industrial Engineering and Operations Management (IEOM) at the School of Management and Engineering (SME) of Nanjing University (NJU). Holding a Ph.D. in Operations Research from the University of Illinois at Urbana-Champaign (UIUC) and postdoctoral experience at Johns Hopkins University (JHU), their research bridges optimization theory with machine learning applications.
- Ph.D. from UIUC's Industrial and Enterprise Systems Engineering (ISE) Department (2017-2022)
- Postdoctoral Fellow at JHU's Applied Mathematics and Statistics Department (2022-2024)
- Visiting Ph.D. at ETH Zurich's Optimization & Decision Intelligence Group
Research focuses on stochastic optimization, nonconvex optimization, and minimax optimization with applications to federated learning and the intersection of machine learning and operations research. Their recent work establishes theoretical bounds for optimization algorithms in minimax settings.
Publication trends reveal expertise in distributed optimization, communication efficiency, and algorithm stability. Key venues include AISTATS, ICLR, NeurIPS, and UAI conferences, with technical reports on arXiv and PMLR proceedings.
- 2025 ICLR Blogpost on optimization bounds
- 2024 ICLR/ICML algorithm stability analysis
- 2022 UAI complexity bounds
- 2020 NeurIPS series on optimization frameworks
Awarded the Acheson J. Duncan Fund (2024) and "Yu Qian" Scholarship (2016), Siqi also serves as reviewer for top venues like NeurIPS, AAAI, and SIOPT journal. Teaching portfolio includes optimization theory, probability, and linear algebra applications at both JHU and ETH Zurich.
