Tianjiao Li

About Me

I am a fourth-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.

Research Interests

My research focuses on the design and analysis of efficient algorithms for Nonlinear Optimization, Stochastic Optimization, and Reinforcement Learning. I am particularly interested in

• Policy optimization and policy evaluation in reinforcement learning
• Parameter-free methods for convex and nonconvex optimization
• Stochastic optimization methods for statistical learning and machine learning
• Real-life applications: healthcare, E-commerce, financial industry, etc

Publications

Awards

• Shabbir Ahmed PhD Fellowship for Excellence in Research
  • Awarded annually to one Ph.D. student (co-winner) in ISyE for research in optimization
• First Place, Best Poster Award, Georgia Statistics Day 2023

Talks and Presentations

• 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
• 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
• Guest Lecturer, ISyE 8803, Georgia Tech, Atlanta, GA, Feb 2024
  • Course: Optimization Methods for Reinforcement Learning
  • Instructor: Guanghui (George) Lan
  • Responsibility: 8 Lectures in optimization methods for policy evaluation and average-reward MDPs
• 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
• 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

Conference Reviewers

• Annual Conference on Learning Theory (COLT) 2022-2024

Journal Reviewers

• SIAM Journal on Optimization
• Mathematical Programming
• Computational Optimization and Applications

Powered by Jekyll and Minimal Light theme.