CV

Education

2023–2027	Ph.D. (Ongoing), Computer Science, IST Austria (ISTA) Advisor: Prof. Krishnendu Chatterjee Research Areas: Optimization, Reinforcement Learning, Games, & Machine Learning Ph.D. (Co-registered), Statistics and Operations Research, Faculty of Business, Economics and Statistics, University of Vienna
2018–2022	B.Eng., Computer Science, Zhejiang University GPA: 3.92/4.00, Rank: Top 10%

Research Statement

My research focuses on the intersection of optimization, games, and machine learning. Specifically, I have been working on the following topics:

[3, 6, 7, 9]: Complexity and algorithms for sequential decision-making problems.
[5, 8]: Efficient protocols for multiplayer games.
[4, 2]: Communication-efficient algorithms for distributed consensus optimization.

Employments

Jun 2023–Sep 2023	Research Assistant @ MBZUAI \| Abu Dhabi, UAE Advisor: Prof. Samuel Horvath and Prof. Martin Takac Contributions: Developed the first non-convex speedup results of Local SGD and SCAFFOLD (two important algorithms in federated learning) Written a first author paper [4] at AISTATS'2025
Jan 2022–May 2022	Research Intern @ Tencent ARC Lab \| Shenzhen, China Project: Pretrained Model Selection for Downstream Tasks Contributions: Developed a practical tool for the selection of pretrained model checkpoints for various downstream tasks

Publications

Also see my Google Scholar page. '@' stands for alphabetical order.

[9] Ali Asadi@, Krishnendu Chatterjee@, Ruichen Luo@. (2026). Algorithms with Smoothed Polynomial-Time Complexity for Deterministic Discounted-sum and Mean-payoff Games. In Preparation.
[8] Ruichen Luo, Anton Rodomanov, Sebastian Stich. (2026). Efficient Gradient Methods for Distributed Saddle Problems. In Submission to NeurIPS'2026.
[7] Krishnendu Chatterjee@, Ruichen Luo@, Raimundo Saona@, Jakub Svoboda@. (2026). Improved Algorithmic Analysis for Stochastic Games. In Submission to NeurIPS'2026.
[6] Krishnendu Chatterjee@, Ruichen Luo@, Raimundo Saona@, Jakub Svoboda@. (2026). Algorithms for Linear Equations with Min and Max Operators under (Absolutely) Halting Condition. In Submission to NeurIPS'2026. [Slides]
[5] Ruichen Luo, Sebastian U Stich, Krishnendu Chatterjee. (2026). Monotone Near-Zero-Sum Games. In ICLR'2026. [Poster] [Slides]
[4] Ruichen Luo, Sebastian U Stich, Samuel Horvath, Martin Takac. (2025). Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis. In AISTATS'2025. [Poster] [Slides]
[3] Krishnendu Chatterjee@, Ruichen Luo@, Raimundo Saona@, Jakub Svoboda@. (2025). Linear Equations with Min and Max Operators: Computational Complexity. In AAAI'2025 (Oral). [Slides]
[2] Ali Zindari, Ruichen Luo, Sebastian U Stich. (2023). On the Convergence of Local SGD under Third-Order Smoothness and Hessian Similarity. In OPT'2023 NIPS Workshop.
[1] Longqian Huang, Ruichen Luo, Xu Liu, Xiang Hao. (2022). Spectral Imaging with Deep Learning. Light: Science & Applications. Vol. 11(61). [Top Downloaded Paper 2022, 2023, 2024]

Research Visits

Oct 2025–Nov 2025 Mar 2026–Apr 2026	CISPA Helmholtz Center \| St. Ingbert, Germany Host: Dr. Sebastian U Stich Collaboration Project: Minimax Optimization with General Conditioning [8]
Oct 2024–Dec 2024	CISPA Helmholtz Center \| St. Ingbert, Germany Host: Dr. Sebastian U Stich Collaboration Project: Monotone Near-Zero-Sum Games [5]

Student Mentorships

Feb 2025–Jun 2025	Elahe Tohidi, Scientific Intern @ ISTA
Aug 2024–Dec 2024	Yizhou Mao, Scientific Intern @ ISTA

Talks and Presentations

Mar 12, 2026	Efficient Gradient Methods for Distributed Saddle Problems FORSYTE & ISTA Joint Seminar, Vienna, Austria
Nov 01, 2025	From Simple Stochastic Games to Linear Equations with Min and Max Operators: Complexities and Algorithms Control Software Systems Group Seminar, Kaiserslautern, Germany
Oct 10, 2025	Monotone Near-Zero-Sum Games: A Generalization of Convex-Concave Minimax Machine Learning and Optimization Group Meeting, St. Ingbert, Germany
Jun 30, 2025	New Nonconvex Analysis of LocalSGD and SCAFFOLD Monday Session D, EUROPT'2025, Southampton, England
Jun 23, 2025	The Complexity of A Class of Linear Equations with Min and Max Operators Monday Session D, EURO'2025, Leeds, England
Oct 23, 2024	New Nonconvex Analysis of LocalSGD and SCAFFOLD Machine Learning and Optimization Group Meeting, St. Ingbert, Germany

Teaching

TA for

Formal Methods: Algorithmic Approaches @ ISTA (2026 Spring)

Academic Services

Reviewer for

NeurIPS'2023, NeurIPS'2025, ICLR'2026, NeurIPS'2026