About Me
I am a Research Scientist on the Alignment team at FAIR, Meta. I work with Dr. Jason Weston on Reasoning, Memory, and Alignment of Large Language Models.
I recently obtained a PhD in Computer Science from the University of North Carolina at Chapel Hill, advised by Prof. Mohit Bansal. My PhD was supported by a Google PhD Fellowship and a Rebecca and Munroe Cobey Fellowship.
I study Machine Learning and NLP. Broadly, I am interested in developing language agents that can perform complex, multi-step reasoning and planning tasks (often referred to as the System 2 Reasoning). I see human language as the ideal medium for reasoning, communication, and collaboration among agents. Below are some of the main topics that I’ve worked on (publications here):
- LLM-as-a-Judge and Generative Reward Models
- Reasoning and Planning in Multi-Agent systems
- Attributable Generative Reasoning
- Structured Reasoning over Implicit Knowledge
- Deductive Reasoning
Recent News
- May 2025: J1, new paper on RL recipes for training LLM-as-a-Judge, is now on arXiv.
- May 2025: Organizing the RAM2 workshop, co-located with COLM 2025. Submit your papers soon!
- May 2025: EvalPlanner is accepted to ICML 2025.
- January 2025: EvalPlanner, my first work out of FAIR, is now on arXiv.
- January 2025: System-1.x is accepted to ICLR 2025.
- August 2024: Joined FAIR at Meta as a Research Scientist.
- July 2024: System-1.x, my final PhD paper, is out on arXiv.
- May 2024: ReConcile is accepted to ACL 2024.
- May 2024: MAGDi is accepted to ICML 2024.
- April 2024: Defended a thesis on “Multi-step Reasoning over Natural Language”. Check it out here.
- March 2024: Branch-Solve-Merge is accepted to NAACL 2024.
- February 2024: New pre-print on Structured Distillation from Multi-agent Interaction Graphs.
- October 2023: New pre-print from my FAIR Internship on Branch-Solve-Merge for improving LLM Evaluation and Generation.
- October 2023: ReCEval on evaluating CoT rationales is accepted to EMNLP 2023.
- September 2023: New pre-print out! Diverse LLMs improve reasoning via multi-round discussion and convincing each other.