About Me
I am a fifth-and-final-year PhD student in Operations Research at ISyE, Georgia Tech. I am fortunate to be advised by Prof. Guanghui (George) Lan and Prof. Ashwin Pananjady. I also obtained an M.S. in Quantitative and Computational Finance from the same institute. Before that, I obtained my B.S. in Mathematics from Fudan University.
I am on the 2024-2025 academic job market!
Research Interests
My research focuses on the design and analysis of efficient algorithms for Nonlinear Optimization, Stochastic Optimization, and Dynamic Decision-Making. I am particularly interested in
- Parameter-free methods for convex and nonconvex optimization
- Stochastic optimization for statistical learning and machine learning
- Policy optimization and policy evaluation for Markov decision processes
- Applications: healthcare, E-commerce, finance, etc
Publications
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Tianjiao Li, Guanghui Lan
Under second-round review, Mathematical Programming Series A. Initial version submitted in Oct 2023.
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Sasila Ilandarideva, Anatoli Juditsky, Guanghui Lan, Tianjiao Li (alphabetic order)
Mathematical Programming Series A, 2024. DOI:10.1007/s10107-024-02138-4
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Tianjiao Li, Feiyang Wu, Guanghui Lan
Accepted for publication, Mathematics of Operations Research, 2024
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Tianjiao Li, Ziwei Guan, Shaofeng Zou, Tengyu Xu, Yingbin Liang, Guanghui Lan
Operations Research Letters, vol. 54, 107107, 2024
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Tianjiao Li, Guanghui Lan, Ashwin Pananjady
SIAM Journal on Mathematics of Data Science, vol. 5, no. 1, pp. 174-200, 2023
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Georgios Kotsalis, Guanghui Lan, Tianjiao Li (alphabetic order)
SIAM Journal on Optimization, vol. 32, no. 3, pp. 2041-2073, 2022
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Georgios Kotsalis, Guanghui Lan, Tianjiao Li (alphabetic order)
SIAM Journal on Optimization, vol. 32, no. 2, pp. 1120-1155, 2022
Preprints
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Guanghui Lan, Tianjiao Li (alphabetic order)
Preprint at arXiv:2410.01979, 2024
Awards
Teaching
- Course Instructor, Summer 2024, Georgia Tech
- Course: Statistics and Applications (ISyE 3770)
- Description: Introductory probability and statistics course for engineering and computer science students
- Class size: 64 (26 on campus + 38 online)
- Teaching evaluation: 4.8/5.0 (response rate: 56%)
- Respect for students: 4.8/5.0
- Inclusiveness: 4.9/5.0
- Communicated how to succeed: 4.7/5.0
- Availability: 4.9/5.0
- Stimulates interest: 4.6/5.0
- Clarity: 4.5/5.0
- Feedback helpfulness: 4.8/5.0
- Guest Lecturer, Fall 2024, Georgia Tech
- Course: Computational Data Analysis / Machine Learning (ISyE 6740)
- Instructor: Guanghui (George) Lan
- Responsibility: 2 Lectures in machine learning and data analysis
- Guest Lecturer, Spring 2024, Georgia Tech
- Course: Optimization Methods for Reinforcement Learning (ISyE 8803)
- Instructor: Guanghui (George) Lan
- Responsibility: 8 Lectures in optimization methods for policy evaluation and average-reward MDPs
Talks and Presentations
- INFORMS Annual Meeting, Seattle, WA, Oct 2024
- Session: ME34 - First-Order Methods in Continuous and Stochastic Optimization
- Location: Summit - 425
- Time: Monday, October 21, 4:00 PM - 4:20 PM
- Title: A Simple Uniformly Optimal Method without Line Search for Convex Optimization
- Cornell ORIE Young Researchers Workshop, Ithaca, NY, Oct 2024
- Session: Optimization I
- Title: A Simple Uniformly Optimal Method without Line Search for Convex Optimization
- YinzOR Student Conference, CMU Tepper School of Business, Pittsburg, PA, Aug 2024
- Poster Presentation: Accelerated Stochastic Approximation with State-Dependent Noise
- Won the Second Place in the poster competition
- International Symposium on Mathematical Programming (ISMP 2024), Montreal, Canada, Jul 2024
- Session: Advances in Stochastic First-Order Methods
- Title: A Simple Uniformly Optimal Method without Line Search for Convex Optimization
- DAO Team Seminar at Laboratoire Jean Kuntzmann, Université Grenoble Alpes, Grenoble, France, May 2024
- Title: A Simple Uniformly Optimal Method without Line Search for Convex Optimization
- INFORMS Optimization Society Conference, Houston, TX, Mar 2024
- Session: Advances in Continuous Optimization Algorithms
- Title: A Simple Uniformly Optimal Method without Line Search for Convex Optimization
- INFORMS Annual Meeting, Phoenix, AZ, Oct 2023
- Session: Recent Advances in Policy Optimization and Reinforcement Learning
- Title: Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation
- Georgia Statistics Day 2023, Atlanta, GA, Oct 2023
- Poster Presentation: Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation
- Won the First Place in the poster competition
- SIAM Conference on Optimization, Seattle, WA, May 2023
- Session: New Sparse Optimization
- Title: Accelerated Stochastic Approximation with State-Dependent Noise
- INFORMS Annual Meeting, Indianapolis, IN, Oct 2022
- Session: Reinforcement Learning Theory
- Title: Stochastic First-Order Methods for Average-Reward Markov Decision Processes
- ISyE Ph.D. Student Seminar, Atlanta, GA, Sept 2022
- Title: Stochastic First-Order Methods for Average-Reward Markov Decision Processes
- Asilomar Conference on Signals, Systems, and Computers, Online, Nov 2021
- Session: Theory of Reinforcement Learning
- Title: Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process
- INFORMS Annual Meeting, Online, Oct 2021
- Session: Stochastic Optimization in Machine Learning
- Title: Simple and Optimal Methods for Stochastic Variational Inequalities
Services
Journal Reviews
Conference Reviews
- Annual Conference on Learning Theory (COLT) 2022-2024
Session Organization
- INFORMS Annual Meeting 2024, Seattle, WA, Oct 2024
- Session: Advances in Continuous Optimization Algorithms
- Session: Advances in Non-Smooth Optimization
- International Symposium on Mathematical Programming (ISMP 2024), Montreal, Canada, Jul 2024
- Session: Advances in First-Order Methods for Continuous and Stochastic Optimization
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