Teaching language models to reason, and checking the work.
I am a Ph.D. candidate in Computer Science and Engineering at Pennsylvania State University, advised by Prof. Vasant Honavar, graduating in January 2027. My research is on reasoning in large language models. I build agentic workflows and evaluation harnesses that find where models fail, generate targeted synthetic data to fix those failures, and feed it back through SFT, RL, and pretraining.
Most recently I was a Research Scientist Intern at a stealth AI startup in New York, training reward models for creative judgment. Before that I worked directly with Google's Gemini Data team on the Gemini IMO 2025 project, and my work on LLMs for mathematics is supported by an unrestricted research gift from Google.
News
- Oct 2026
- Splicing Hints into Reasoning Traces is on arXiv and under review at ICLR 2027.
- Aug 2026
- Wrapped up a six-month Research Scientist internship at a stealth AI startup in New York.
- Mar 2026
- Joined a stealth AI startup in New York to work on reward models for creative judgment.
- Oct 2025
- RefGrader and CombiGraph-Vis accepted to the Math-AI Workshop at NeurIPS 2025.
- Jul 2025
- Worked with Google's Gemini Data team as an external research collaborator on Gemini's IMO 2025 gold-medal-level run.
- May 2025
- Secured a $100K unrestricted research gift from Google for my research on LLM mathematical reasoning.
- Apr 2025
- Brains vs. Bytes accepted to COLM 2025.
Publications
Experience
- Developed agentic workflows and evaluation harnesses for open-ended creative content generation, with a focus on generating synthetic reasoning data for creativity.
- Built pipelines that transform outputs from agentic workflows into training data, studying the interaction between mid-training and post-training for creative reasoning capabilities.
- Trained reasoning and non-reasoning reward models for creative judgment and led data curation for reward modeling in the creative judgment domain.
- Worked directly with Google's Gemini Data team, led by Dr. Vahab Mirrokni, as an external research collaborator; designed and delivered high-quality verifiable data and evaluations that contributed to Gemini's gold-medal-level performance at the International Mathematical Olympiad (IMO).
- Designed agentic workflows for math problem solving and high-quality mathematical data generation; built and ran evaluation harnesses for contest-level math (benchmarks, metrics, ablations, failure analysis).
- Worked in the Fabrication Technology group under the supervision of Martin Marinack; tackled several data-driven optimization problems to improve Arconic's fabrication processes (welding, rolling, and casting).
- Worked on fundamental aspects of reinforcement learning, focusing on multi-agent, adversarial, and human-centered reinforcement learning.
- Studied how to design an AI agent that can robustly cooperate with agents of unknown types encountered only after deployment.
- Developed a compression algorithm that allowed Faragostar to retain its official documents while minimizing storage requirements.
- Configured infrastructure for Elasticsearch, enabling more efficient and complex searches than the previous SQL-based search tool.
- Taught number theory to academically gifted students.
Education
Honors
Interests
- LLMs for math problem solving
- Agentic workflows for synthetic data generation
- Reasoning in LLMs
- Post-training, reward modeling, and LLM-as-a-judge
- Reinforcement learning
- Optimization in machine learning
Outside research I play tennis and badminton, which I have done since I was nine, and guitar, acoustic and electric, mostly classic rock.