Tianjiao Li

About Me

I am a Postdoctoral Researcher and Lecturer at the MIT Sloan School of Management, working with Prof. Swati Gupta.

I obtained my PhD in Operations Research from Georgia Tech ISyE in Summer 2025, where I was fortunate to be advised by Prof. Guanghui (George) Lan and Prof. Ashwin Pananjady. I also got 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 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

PhD Dissertation: New Accelerated Methods for Optimization and Reinforcement Learning

Publications

Preprints

Awards

• Alice and John Jarvis Best Student Paper Award, 2024
  • Awarded annually to one Ph.D. student in ISyE across all disciplines
• Shabbir Ahmed PhD Fellowship for Excellence in Research, 2023
  • Awarded annually to one Ph.D. student in ISyE for research in optimization

• Second Place, Poster Competition, YinzOR Student Conference 2024

• First Place, Best Poster Award, Georgia Statistics Day 2023

Teaching

• Course Instructor, Fall 2025, MIT
  • Course: Introduction to Mathematical Programming (15.081/6.7210)
  • Description: MIT’s doctoral introductory optimization course for ORC and other MIT PhD programs
• 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

• International Conference on Continuous Optimization (ICCOPT 2025), Los Angeles, CA, Jul 2025
  • Session: Advances in Solving Large-Scale Problems: Accelerated Methods and Sharp Analyses
  • Title: Accelerated Stochastic Approximation with State-Dependent Noise
• ORIE Colloquium, Cornell University, Ithaca, NY, Feb 2025
  • Title: Universal Parameter-Free Methods for Convex, Nonconvex, and Stochastic Optimization
• STOR Colloquium, UNC Chapel Hill, Chapel Hill, NC, Jan 2025
  • Title: Universal Parameter-Free Methods for Convex, Nonconvex, and Stochastic Optimization
• INFORMS Annual Meeting, Seattle, WA, Oct 2024
  • Session: First-Order Methods in Continuous and Stochastic Optimization
  • 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

• SIAM Journal on Optimization
• Mathematical Programming
• Annuals of Statistics
• Computational Optimization and Applications
• Optimization Letters
• Journal of Global Optimization
• Journal of Optimization Theory and Applications
• Operations Research Letters

Conference Reviews

• Annual Conference on Learning Theory (COLT) 2022-2025

Session Organization

• International Conference on Cotinuous Optimization (ICCOPT 2025), Los Angeles, CA, Jul 2025
• Session: Recent Advances in Stochastic First-Order Methods
• 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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