I joined Computer Science & Engineering (CSE) Department at Washington University in St. Louis (WashU) in 2024 as an Assistant Professor. I received my Ph.D. degree in Computer Science Department, UIUC, advised by Prof. Jiawei Han. After that, I visited UW as a researcher and worked with Prof. Hanna Hajishirzi. Prior to UIUC, I received my Bachelor Degree in Electronic Engineering in Tsinghua University in 2018. My research interest broadly lies in the intersection of natural language processing and machine learning, and I am especially interested in understanding the properties of language models as well as improving their trustworthiness and efficiency.

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Recent Research Interests

• Training and Inference Efficiency of Large Language Models: I work on improving language model training and inference efficiency, including collaborative decoding, test-time scaling, speculative decoding, parallel thinking, long in-context learning, and efficient RLVR training.
• Large Language Model Reasoning: I investigate how large language model reasoning capabilities can be self-improved through SFT, RL (text and visual) with broader exploration, and weak supervision, as well as their evaluation on structured tasks like crossword puzzles.
• Large Language Model Alignment: I work on improving language model calibration to align their confidence with performance, including reward calibration in RLHF and calibrated process rewards.
• Large Language Model Factuality: I build methods for integrating factual knowledge within language models, including graphs(1), ontologies(1,2,3), entities(1,2), retrieval(1), and applying them for downstream tasks(1) in low-resource setting.
• Data Efficiency for Language Model Training: I study how to better fine-tune language model with limited training data, including data self-generation(1,2,3,4), denoising distant-supervision, integrating metadata, and data efficiency for multimodal reward models.
• Representation Learning: I study how the text embedding space could be regularized in different circumstances (category-based, joint-categories learning, contextualized, etc.)

Honors and Awards

NSF CAREER Award 2026
AAAI New Faculty Highlight Program 2026
Microsoft Research PhD Fellowship 2021-2023
C.W. Gear Outstanding Graduate Award
Chirag Foundation Graduate Fellowship
Outstanding Graduates, Tsinghua University 2018
Academic Excellence Scholarship, Tsinghua University 2015-2017
China National Scholarship (Top 1%) 2016
Samsung Scholarship 2015