About

I'm a fifth-year PhD student at the University of Pennsylvania advised by Professors Mayur Naik and Eric Wong. I spent the past two summers at AWS AI (Fundamental Research Team) working with Matthew Trager and Stefano Soatto on uncertainty quantification and experience-guided reasoning for agents. My research is supported by the NSF Graduate Research Fellowship Program.

My research aims to make AI systems reason reliably and behave as intended. I study the interface between foundation models and programs: which parts of a task should be left to a model's semantic flexibility, which should be handed to executable structure, and where to look for evidence that the system actually did what its output implies. My work is organized around a hierarchy of static, generated, and agentic programs, moving from fixed solvers and symbolic interfaces, to instance-specific programs and inference-time strategies, to agents whose behavior must be audited across traces. Most recently, I've been using the records agents leave behind to make them safer, more reliable, and cheaper to run.

Research Summary

The Interface Between Foundation Models and Programs

Static programs

Translate unstructured inputs into artifacts that existing solvers can execute.

Pre-print '25 ICML '24 ICLR Tiny '23 ACL '25 AACL '24

Generated programs

Generate and check instance-specific programs and adaptive strategies.

Pre-print '25 NeurIPS '25

Agentic programs

Audit systems whose behavior plays out across tools, state, and traces.

Pre-print '26

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