Welcome!

Hello, I’m Yao Zhao, a 5th-year PhD student at the University of Arizona in computer science working with Dr. Kwang-Sung Jun. I mainly have fun exploring multi-armed bandits / RL / adaptive experimentation, and recently RLHF.

I am seeking my next position. Please contact me if you see a fit.

Selected Papers

Fixing the Loose Brake: Exponential Tail Bounds for Stopping Time in Best Arm Identification

• (co 1st author: ^† ) Kapilan Balagopalan^†, Tuan Nguyen^†, Yao Zhao^†, Kwang-Sung Jun
• In International Conference on Machine Learning (ICML), 2025, arXiv

Adaptive Experimentation When You Can’t Experiment

• Yao Zhao, Kwang-Sung Jun, Tanner Fiez, Lalit Jain
• In Conference on Neural Information Processing Systems (NeurIPS), 2024, Official
• Also accepted and presented at Conference on Digital Experimentation (CODE@MIT), Cambridge, 2023

Revisiting Simple Regret: Fast Rates for Returning a Good Arm

• Yao Zhao, Connor Stephens, Csaba Szepesvári, Kwang-Sung Jun
• In International Conference on Machine Learning (ICML), 2023, Official
• Also accepted and presented at
  • Quantifying Uncertainty: Stochastic, Adversarial, and Beyond workshop, Data-Driven Decision Processes, Simons Institute for the Theory of Computing, Berkeley, 2022
  • Information Theory and Applications Workshop, San Diego, 2024

Feel free to reach out if you’d like to discuss any of these works!

Industry Experiences

Applied Scientist Intern, Amazon Summer 2023, summer 2024

• Mentors: Dr. Lalit Jain, Dr. Tanner Fiez, Dr. Bibek Adhikari
• Project blog: Adaptive experimentation when you can’t experiment
• Worked with cross-functional teams of ML scientists, economists.
• Developed a causal inference ML solution for optimizing Amazon’s membership tier under confounding and compliance issues to support key products decision making.
• Developed a LLM augmented experimentation solution.

Teaching Experiences

• Principles of Data Science (Undergrad-level class with 100+ students) Fall, 2024

• Algorithms (Undergrad-level class with 60 students) Spring, 2023

• Machine Learning (Grad-level class with 100+ students, Excellent Teaching Assistant Award) Spring, 2019

• Probability and Statistics (Undergrad-level class with 100+ students) Spring, 2018

Professional Services

I’ve served as a reviewer for major ML conferences, including ICML, NeurIPS, AISTATS, ICLR etc.

Award

• NeurIPS Scholar Award 2024

• Travel Award for ICML 2023

• Graduate College Fellowship 2021

• Excellent Teaching Assistant Award 2019