CV

Yale University

Two-year M.S. in Computer Science

University of Michigan

B.S. in Data Science · summa cum laude · GPA 4.00 / 4.00

Shanghai Jiao Tong University

B.S. in Electrical and Computer Engineering

Alibaba Inc.

Research Intern · AliStar Top Talent Program · Business Arena: Benchmarking LLM Agents in a Realistic Marketplace

We build a long-horizon, realistic business world to measure whether agents can operate end-to-end businesses.

Snap Inc. & Yale University

Student Researcher · FlexRec: Adapting LLM-based Recommenders for Flexible Needs via Reinforcement Learning

Real user needs are diverse, and one item can carry different value when the need changes. We introduce FlexRec, a post-training framework that aligns LLM recommenders across multiple needs.

Carnegie Mellon University

Machine Learning Research Intern · DATE-LM: Benchmarking Data Attribution Evaluation for Large Language Models

Data attribution promises to explain how training data shapes LLM behavior, but existing evaluations are fragmented. We introduce DATE-LM, a unified benchmark for comparing attribution methods across practical LLM applications.

UIUC & USC

Machine Learning Research Intern · Detecting and Filtering Unsafe Training Data via Data Attribution with Denoised Representation

Small amounts of unsafe training data can meaningfully change model behavior, while fixed moderation categories can miss emerging risks. We use data attribution to connect harmful behavior back to influential training examples and support targeted filtering.

University of Michigan & UIUC

Student Researcher · dattri: A Library for Efficient Data Attribution

Data attribution methods are useful but difficult to implement and compare consistently. We introduce dattri, an open-source PyTorch library for developing, benchmarking, and deploying efficient data-attribution methods.

University of Michigan

Student Researcher · Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science

AI and scientific research are advancing quickly, but useful methods and real scientific needs do not always meet. We map the AI4Science literature at scale to reveal these gaps and surface opportunities for cross-disciplinary collaboration.