About me

Hi, I am a fourth-year Ph.D. candidate in the Department of Statistics & Data Science at University of California, Los Angeles (UCLA). I am a member of the Lab for Statistics, Computing, Algorithms, Learning, and Economics (SCALE) , advised by Prof. Xiaowu Dai.

Prior to my Ph.D., I earned two Bachelor of Science degrees in Probability & Statistics and Management Science at University of California, San Diego (UCSD), as well as a Master of Arts in Statistics at Columbia University, where I was advised by Prof. Michael E. Sobel.

My research lies at the intersection of Machine Learning and Economics, focusing on:
(1) Market-driven mechanisms that enable intelligent decision-making and fair resource allocation in digital economies, such as data markets and privacy trading, giving users control over their data instead of passively surrendering it.
(2) AI systems that reason, adapt, and respond to economic incentives, ensuring they operate efficiently in environments shaped by human behavior and strategic interactions, like LLM-powered ads auctions and retrieval-based uncertainty quantification.

My first name is pronounced as Yee-ung-Chee.

Grants

  • Division of Graduate Education Award, UCLA, 2023, 2024
  • Recipient of the Statistics Department Chair’s List of Academic Achievements, Columbia, 2020
  • JSM 2020 Registration Award, Columbia, 2020
  • BS magna cum laude in Management Science, UCSD, 2019

Research

My research explores the two-way interaction between AI and economic systems, focusing on (1) how AI can improve economic decision-making and (2) how economic principles can shape AI-driven markets.

As digital assets like data, privacy, and AI-generated content become more valuable, traditional markets must adapt. I develop mechanisms that enable efficient trade while ensuring fairness, privacy, and strategic incentives. Currently, I am designing fair yet profitable data marketplaces, ensuring efficient trading mechanisms while aligning incentives for buyers and sellers. Looking ahead, I aim to develop a privacy market where users can dynamically control their data exposure—like turning a knob—balancing privacy protection with economic incentives.

At the same time, I study how AI can integrate economic reasoning to make better decisions under uncertainty. For example, retrieval-based AI can improve predictive reliability by drawing on external knowledge. In healthcare, this could mean retrieving past diagnoses made by human professionals from similar cases to assess uncertainty in a new diagnosis, ensuring AI-generated recommendations are not only data-driven but also grounded in real-world medical expertise. Similarly, in AI-powered chatbots, this goes beyond simply listing the sources —it involves statistically interpreting those sources to quantify the credibility of an answer.

A related concept has been explored in ads auctions, where large language models incorporate bidding mechanisms to optimize AI-generated ad placement. I aim to expand this idea by introducing an economic interpretation of AI-generated value, ensuring that outcomes align with market incentives. For instance, if someone bids $100, the AI-generated ad should deliver at least proportionally greater value compared to a $10 bid. Embedding this principle enhances fairness, reinforces incentives, and strengthens trust in AI-driven markets.

Ultimately, I seek to build scalable, inference-driven AI systems that go beyond processing information to actively reason, adapt, and make economically sound decisions in complex marketplaces shaped by human behavior and strategic interactions. By integrating statistical reasoning to assess uncertainty and economic principles to ensure fairness and incentives, my work seeks to create AI-driven markets that are both efficient and trustworthy. From (1) data markets to (2) privacy trading, where users control their exposure instead of passively surrendering data, to (3) LLM-powered ads auctions and (4) retrieval-based decision-making, I strive to build systems that bridge AI intelligence with economic insight, fostering transparent, adaptable, and value-aligned AI interactions.

Teaching

Courses TA'd

📍 UCLA

  • STATS 102B Introduction to Computation and Optimization for Statistics (Summer 2024)
  • STATS 102A Introduction to Computational Statistics with R (Summer 2024)
  • STATS 100A Introduction to Probability (Fall 2024)
  • STATS 20 Introduction to Statistical Programming with R (Spring 2022)
  • STATS 13 Introduction to Statistical Methods for Life and Health Sciences (Fall 2023, Spring 2024)
  • STATS 10 Introduction to Statistical Reasoning (Summer 2022, Fall 2022, Winter 2023, Spring 2023, Summer 2023, Winter 2024, Winter 2025)

📍 Columbia University

  • STAT S1201 Calculus-Based Introduction to Statistics (Summer 2021)

What Students Say

“Yingqi has been the best TA I have had at UCLA. She is a talented and knowledgeable professional who made the class manageable and always checked in with students. Her discussions were on-point, and she was willing to meet with students after class to address difficulties. You can truly see when someone cares about their profession—Yingqi embodies that and so much more.”

“Ms. Gao is an excellent TA, and I greatly appreciate her. Without her, I would have struggled with the class and missed out on valuable training in statistical methods for real-world problems. Before the class, I was terrified of analyzing large datasets—now, I feel competent and inspired. I’m even considering a minor in statistics because of her. She deserves every raise available to TAs.”

“Yingqi is a fantastic programming and statistics TA. She was always considerate, providing extra resources, recordings, and after-class support. Her ability to simplify complex concepts made a huge difference. I would have struggled with the programming component of this class without her.”

Courses

Here is a selected list of graduate-level classes I have completed that are relevant to my research:

Statistics @UCLA

  • STATS 200A Applied Probability with Prof. Yingnian Wu
  • STATS 200B Theoretical Statistics with Prof. Arash Ali Amini
  • STATS 201A Research Design, Sampling, and Analysis with Prof. Hongquan Xu
  • STATS 201B Modeling and Learning with Prof. Chad Hazlett
  • STATS 201C Advanced Modeling and Learning with Prof. Qing Zhou
  • STATS 202A Statistics Programming with Prof. Frederic Paik Schoenberg
  • STATS 202B Matrix Algebra and Optimization with Prof. Mark S. Handcock
  • STATS 256 Causality with Prof. Chad Hazlett
  • STATS 211 Topics in Economics and MAchine Learning with Prof. Xiaowu Dai
  • STATS M231A Pattern Recognition with Prof. Yingnian Wu

Computer Science or Electrical and Computer Engineering @UCLA

  • CS 269 Reinforcement Learning with Prof. Bolei Zhou
  • ECE 236A Linear Programming with Prof. Christina P Fragouli