Qingchuan (Tony) Yang

Los Angeles, CA

qcyang [at] usc [dot] edu

I am a Ph.D. student in Computer Science at the University of Southern California, most fortunate to be advised by Prof. Sai Praneeth Karimireddy.

My research centers on trustworthy AI, with a focus on the predictive intelligence of large language models: how they reason under uncertainty and produce well-calibrated forecasts of future events. This question runs through Prophet Arena, a live benchmark for LLM forecasting, and into broader work on the calibration, alignment, and privacy of the models we increasingly rely on to make decisions.

Previously, I completed my M.S. in Statistics and my B.S. in Computer Science (with Honors) and Statistics at the University of Chicago, where I was incredibly lucky to be part of SIGMA Lab, led by Prof. Haifeng Xu.

news

Jul 15, 2026	Thinking Machines Lab used Prophet Arena to evaluate forecasting performance in its Inkling release.
May 01, 2026	Joining Sooth Labs as a research intern for summer 2026, hosted by Dr. Russ Salakhutdinov and Dr. Yaser Sheikh.
Apr 30, 2026	EPSVec, on efficient and private synthetic data generation, was accepted to ICML 2026.
Feb 12, 2026	Prophet Arena was featured in The Atlantic, Yahoo Finance, and The Dispatch.
Jan 26, 2026	LLM-as-a-Prophet, our work on Prophet Arena, was accepted to ICLR 2026.

selected publications

ICLR

LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena

Qingchuan (Tony) Yang^*, Simon Mahns^*, Sida Li^*, Anri Gu^*, Jibang Wu, and Haifeng Xu

International Conference on Learning Representations (ICLR), 2026

Featured in The Atlantic, Yahoo Finance, The Dispatch, and Thinking Machines Lab’s Inkling release · 450k+ views on X.

Abstract arXiv BibTeX Website

@article{yang2026prophet,
  title = {LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena},
  author = {Yang, Qingchuan (Tony) and Mahns, Simon and Li, Sida and Gu, Anri and Wu, Jibang and Xu, Haifeng},
  journal = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  note = {<span style="color: #cc0000;">Featured in <a href="https://www.theatlantic.com/technology/2026/02/ai-prediction-human-forecasters/685955/" style="color: #cc0000; text-decoration: underline;">The Atlantic</a>, <a href="https://finance.yahoo.com/news/ai-now-matches-prediction-markets-230346352.html" style="color: #cc0000; text-decoration: underline;">Yahoo Finance</a>, <a href="https://thedispatch.com/article/ai-forecasting-national-security-intelligence/" style="color: #cc0000; text-decoration: underline;">The Dispatch</a>, and <a href="https://thinkingmachines.ai/news/introducing-inkling/" style="color: #cc0000; text-decoration: underline;">Thinking Machines Lab's Inkling release</a> &middot; <a href="https://x.com/ProphetArena/status/1956928877106004430" style="color: #cc0000; text-decoration: underline;">450k+ views on X</a>.</span>}
}

ICML

EPSVec: Efficient and Private Synthetic Data Generation via Data Vectors

Amin Banayeeanzade^*, Qingchuan (Tony) Yang^*, Deqing Fu, Spencer Hong, Erin Babinsky, Alfy Samuel, Anoop Kumar, Robin Jia, and Sai Praneeth Karimireddy

International Conference on Machine Learning (ICML), 2026

arXiv BibTeX

@article{banayeeanzade2026epsvec,
  title = {EPSVec: Efficient and Private Synthetic Data Generation via Data Vectors},
  author = {Banayeeanzade, Amin and Yang, Qingchuan (Tony) and Fu, Deqing and Hong, Spencer and Babinsky, Erin and Samuel, Alfy and Kumar, Anoop and Jia, Robin and Karimireddy, Sai Praneeth},
  journal = {International Conference on Machine Learning (ICML)},
  year = {2026},
}