Biography
I’m Jierui Li, a Ph.D. student at computer science department, University of Texas at Austin, advised by Prof. Raymond Mooney. I obtained my bachelor’s degree in computer science from University of Electronic Science and Technology of China.
My current research focus is learning algorithmic reasoning and code generation for competitive-level challenges with large language models. I’m generally interested in Natural Language Processing(NLP)
Education
University of Electronic Science and Technology of China
Bachelor of Engineering in Computer Science and Technology
Sep 2016 ~ Jun 2020
University of Texas at Austin
Ph.D. Student in Computer Science
Sep 2021 ~ present
Experience
Meta — Menlo Park
Ph.D. SWE Intern | May 2025 – Aug 2025
- Internal Tool: Developed an internal tool to improve the productivity of Meta’s content designers and employees working on Meta-to-Users.
- Achieved 99% recall compared to the original method.
- Improved pipeline speed by 200×.
- Adopted by the Central Production Optimization Team.
Salesforce — Singapore
Ph.D. Intern @ AI Research | May 2024 – Aug 2024
- Code Generation: Improved code generation pipeline with agent-guided tree search.
- Developed CodeTree, a framework for LLM agents to efficiently explore the search space during code generation.
- Outperformed then-SoTA (o1) by +1.9% with 23% token usage.
- Patent in process.
Grammarly — San Francisco
Applied Research Intern | May 2023 – Aug 2023
- Detecting Self-Contradictions in Documents:
- Highlighted the task of document-level contradiction detection.
- Proposed an annotated dataset spanning multiple domains, document lengths, and contradiction types.
- Evaluated SOTA LLMs with new evaluation metrics designed for LLMs.
SUTD StatNLP Lab — Singapore
Research Assistant | Jan 2021 – Aug 2021
- Structured Math Word Problem Solving:
- Proposed a bottom-up solver to deductively reason and solve math word problems (MWPs).
- Parsed MWPs into specially structured formulations to improve deductive reasoning.
Tencent AI Lab — Shenzhen
Research Intern | Sep 2019 – Jun 2020
- Evaluating Explanation Methods for NMT:
- Proposed a simulation-based automatic evaluation method for NMT explanation methods.
- Attention’s Interpretability:
- Analyzed the interpretability of the attention mechanisms in transformer models.
Papers
AlgoSimBench: Identifying Algorithmically Similar Problems for Competitive Programming
Jierui Li and Raymond Mooney
Under ReviewCodeTree: Agent-guided Tree Search for Code Generation with Large Language Models
Jierui Li, Hung Le, Yinbo Zhou, Caiming Xiong, Silvio Savarese, and Doyen Sahoo
NAACL 2025Distilling Algorithmic Reasoning from LLMs via Explaining Solution Programs
Jierui Li and Raymond Mooney
NLRSE 2024ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models
Jierui Li, Vipul Raheja, and Dhruv Kumar
NAACL 2024Explaining Competitive-Level Programming Solutions using LLMs
Jierui Li, Szymon Tworkowski, Yingying Wu, and Raymond Mooney
NLRSE 2023Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction
Zhanming Jie, Jierui Li, and Wei Lu
ACL 2022Evaluating Explanation Methods for Neural Machine Translation
Jierui Li, Lemao Liu, Huayang Li, Guanlin Li, Guoping Huang, and Shuming Shi
ACL 2020Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions
Jierui Li, Lei Wang, Jipeng Zhang, Yan Wang, Bing Tian Dai, and Dongxiang Zhang
ACL 2019