CV

Education

• B.S. in Mathematics and Applied Mathematics, Beijing University of Chemical Technology, 2018
• M.S. in Applied Mathematics and Physics, Kyoto University, 2022
• Ph.D. in Applied Mathematics and Physics, Kyoto University, 2025 (expected)

Work experience

• 2019/03–2020/11: Research Assistant
  • Southern University of Science and Technology
• 2019/06-2019/09: Data Analysis Intern
  • Shenzhen Urban Public Safety and Technology Institute
• 2018/06-2018/08: Data Analysis Intern
  • Huawen Media Industry Innovation Research Institute

Skills

• JLPT N1
• Programming Skill
  • Python
  • Matlab

Papers/Preprint

1) K. Chen, E. H. Fukuda, and H. Sato, Nonlinear conjugate gradient method for vector optimization on Riemannian manifolds with retraction and vector transport, Applied Mathematics and Computation, vol. 486, p. 129001, 2025. doi: 10.1016/j.amc.2024.129001.

2) K. Chen, E. H. Fukuda and N. Yamashita. A proximal gradient method with Bregman distance in multi-objective optimization. Pacific Journal of Optimization. 2024, 20(4): 809-826. doi: 10.61208/pjo-2024-012.

Talks

• Kangming Chen, Ellen Hidemi Fukuda, Riemannian generalized conditional gradient methods, 2024 Annual Meeting of the Japan Society for Industrial and Applied Mathematics (JSIAM), Kyoto, Japan, 2024/09/14-2024/09/16. [Detail]
• Kangming Chen, Ellen Hidemi Fukuda, Generalized Conditional Gradient Method with Three Step Size Strategies on Riemannian Manifolds, The 2024 autumn national conference of Operations Research Society of Japan, Nagoya, Japan, 2024/09/09-2024/09/11. [Detail]
• Kangming Chen, Ellen Hidemi Fukuda, Riemannian conditional gradient methods for composite optimization problems, The 17th SIAM East Asian Section Conference, University of Macau, Macao SAR, China, 2024/06/30. [Detail]
• Kangming Chen, Multiobjective Proximal gradient methods on Riemannian manifolds, The 20th Joint Meeting of the Japan Society for Industrial and Applied Mathematics (JSIAM) Activity Groups, Nagaoka, Japan, 2024/03/04. [Detail]
• Kangming Chen, Ellen Hidemi Fukuda, Hiroyuki Sato, Nonlinear conjugate gradient method for vector optimization on Riemannian manifolds, The 2023 autumn national conference of Operations Research Society of Japan, Nishinomiya, Japan, 2023/09/15. [Detail] [PDF]
• Kangming Chen, Ellen Hidemi Fukuda, Hiroyuki Sato, Nonlinear conjugate gradient method for vector optimization on Riemannian manifolds, RIMS workshop on Mathematical Optimization 2023, Kyoto, Japan, 2023/08/29. [Detail]
• Kangming Chen, Ellen Hidemi Fukuda, Hiroyuki Sato, Nonlinear conjugate gradient method for vector optimization on Riemannian manifolds, The 10th International Congress on Industrial and Applied Mathematics (ICIAM), Tokyo, Japan, 2023/08/23. [Detail]
• Kangming Chen, Ellen Hidemi Fukuda, Nobuo Yamashita, A proximal gradient method with Bregman distance in multi-objective optimization, International Workshop on Continuous Optimization, Tokyo (online), Japan, 2022/12/04. [Detail]

Grants

• 2024/04–2025/03: 京都大学大学院教育支援機構奨励研究員, Kyoto University Division of Graduate Studies SPRING Program supported by the Japan Science and Technology Agency (JST)
• 2022/04–2024/03: Kyoto University Science and Technology Innovation Creation Fellowship supported by the Japan Science and Technology Agency (JST)

Service and leadership

• 2023/04 – present: Vice-president of Kyoto University SIAM Student Chapter
• 2023/07 – present: Member of Operations Research Society of Japan (ORSJ)
• 2024 - present: Member of Japan Society for Industrial and Applied Mathematics (JSIAM)