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 research 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
• Under review in AISTATS

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, arXiv
• 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! In a past life, I had fun with mobile network optimization and learning problems, and I published a few papers in these areas. This application journey brought me here to continue my research and exploration on a deeper and more theoretical level.

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