Welcome!
Applied Scientist at Amazon, specializing in large language models, agentic AI, and model training and evaluation. Work spans mid-training, post-training, reinforcement learning, and evaluation, with a focus on long-context understanding, tool-use, personalization, and reliable multi-turn interactions — leading research initiatives that improve the capability and reliability of customer-facing AI systems.
Ph.D. in Computer Science from Texas A&M University, with 20+ publications (8 first-authored) at NeurIPS, ACL, EMNLP, and NAACL, including HYTREL (NeurIPS Spotlight) and CoMM (NAACL). Holds a US patent; served as Emergency Area Chair for ARR.
Current research focuses on building reliable and adaptive agentic AI systems through data-centric training, reinforcement learning, and systematic evaluation.
Email: chenpei.net@gmail.com
Links: LinkedIn
Google Scholar
Office: Santa Clara, CA
- LLM Mid-training, Post-training, and Reinforcement Learning
- Agentic AI, Tool-use, and Long-context Reasoning
- Model Evaluation, Personalization, and Multi-turn Interactions
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Ph.D. in Computer Science, 2019 - 2024
Texas A&M University
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MS in Finance
Southwestern University of Finance and Economics
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B.Eng. in Simulation Engineering
National University of Defense Technology
News
- 2026: 3 papers on multi-turn modeling (via GRPO), personalization, and agentic systems accepted to ACL 2026. 🎉
- 2026: Serving as Emergency Area Chair for ARR. 🎉
- 2026: US Patent 12,530,529 granted for Domain-specific NER via Graph Neural Networks.
- 2025: 6 papers accepted to NAACL-2025, ACL-2025, and EMNLP-2025, covering long-context modeling, agents, RAG, and post-training data flywheel. ✨
- 2024: First-authored long paper (CoMM) accepted to NAACL-2024 — a pioneering multi-agent prompting framework for complex LLM reasoning. 👋
- 2023: First-authored paper (HYTREL) accepted to NeurIPS-2023 as a Spotlight presentation (top 5%). ✨
Selected Publications
LLM Training & Alignment
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HYTREL: Hypergraph-enhanced Tabular Data Representation Learning
Pei Chen, Soumajyoti Sarkar, Leonard Lausen, Balasubramaniam Srinivasan, Sheng Zha, et al.
NeurIPS 2023, Spotlight (top 5%) -
Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning
Ming Li, Pei Chen, Chenguang Wang, Hongyu Zhao, Yijun Liang, et al.
ACL 2025, Findings -
ItD: Large Language Models Can Teach Themselves Induction through Deduction
Wangtao Sun, Haotian Xu, Xuanqing Yu, Pei Chen, Shizhu He, et al.
ACL 2024, long paper -
Aligning Large Language Models with Implicit Preferences from User-Generated Content
Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Pei Chen, et al.
ACL 2025, long paper
Agent
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CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving
Pei Chen, Shuai Zhang, Boran Han
NAACL 2024, Findings -
Hephaestus: Improving Fundamental Agent Capabilities of LLMs through Continual Pre-Training
Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Pei Chen, et al.
NAACL 2025, long paper -
Improving LLMs Function Calling and Interpretability via Guided-Structured Templates
Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Pei Chen, et al.
EMNLP 2025, long paper
RAG & Long-context & Personalization
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LongLeader: A Comprehensive Leaderboard for LLMs in Long-context Scenarios
Pei Chen, Hongye Jin, Cheng-Che Lee, Rulin Shao, Jingfeng Yang, et al.
NAACL 2025, long paper -
ALERT: An LLM-powered Benchmark for Automatic Evaluation of Recommendation Explanations
Yichuan Li, Xinyang Zhang, Chenwei Zhang, Mao Li, Pei Chen, et al.
NAACL 2025, long paper -
UniConv: Unifying Retrieval and Response Generation for LLMs in Conversations
Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Pei Chen, et al.
ACL 2025, long paper