About
I am a Master's student at Harvard studying Data Science, with a background in Math, Physics, and Computer Science from the University of Michigan. I am broadly interested in AI safety and the science of deep learning.
At Harvard, I am co-advised by Prof. Finale Doshi-Velez and Prof. David Alvarez-Melis for my thesis on unifying and improving the science of AI safety evaluations. I am also a Spring 2026 CBAI Research Fellow, working with Dr. Laura Ruis on chain-of-thought (CoT) monitoring.
This summer, I will be interning at the Berkman Klein Center for Internet and Society at Harvard, where I will be investigating the reliability of AI safety evaluations and their readiness to support emerging regulatory frameworks. I will also be working part-time as a Machine Learning Fellow at 10a Labs on adversarial red-teaming and model evaluations.
Research & Publications
From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries
We introduce a novel course recommendation system that combines large language models with semantic search to bridge natural language queries and course descriptions. Our two-stage approach generates idealized course descriptions from student interests, then uses embedding similarity to identify relevant courses. Empirical evaluation demonstrates the system's ability to provide contextually appropriate recommendations with explanatory rationales.