Bin Gao
Scholar

Bin Gao

Google Scholar ID: Q9uKXacAAAAJ
Academy of Mathematics and Systems Science, Chinese Academy of Sciences
Riemannian optimizationTensor computationParallel computingMachine learning
Citations & Impact
All-time
Citations
479
 
H-index
12
 
i10-index
13
 
Publications
20
 
Co-authors
19
list available
Publications
20 items
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
  • - Publications:
  • - 2025, SIAM Journal on Matrix Analysis and Applications, “Optimization on product manifolds under a preconditioned metric”
  • - 2025, ICML2025, “Distributed retraction-free and communication-efficient optimization on the Stiefel manifold”
  • - 2025, ISIT2025, “Riemannian optimization for Holevo capacity”
  • - 2025, ICLR2025, “LancBiO: dynamic Lanczos-aided bilevel optimization via Krylov subspace”
  • - 2025, AISTATS2025, “Bilevel reinforcement learning via the development of hyper-gradient without lower-level convexity”
  • - 2024, Mathematical Programming, “Low-rank optimization on Tucker tensor varieties”
  • - 2024, Journal of Machine Learning Research, “Infeasible deterministic, stochastic, and variance-reduction algorithms for optimization under orthogonality constraints”
Research Experience
  • - Postdoc, UCLouvain, 2019-2021, Host: P.-A. Absil
  • - Postdoc, University of Münster, 2021-2022, Host: Benedikt Wirth
  • - (Tenure-track) Associate Professor, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Sep 2022 – Mar 2025, Academic staff of LSEC and ICMSEC
  • - (Tenured) Associate Professor, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Apr 2025 – Present, Academic staff of ICMSEC
Education
  • - B.Sc. in Mathematics, 2014, Sichuan University, China
  • - Ph.D. in Applied Mathematics, 2019, University of Chinese Academy of Sciences
Background
  • He is an associate professor at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. His research interests include numerical methods for optimization on manifolds and their applications, tensor computation, machine learning, and parallel/distributed optimization.
Miscellany
  • Blog: popman blog