Stanford University, School of Engineering

Master's in Computer Science

Advisors: Dorsa Sadigh (ILIAD Lab), Fei-Fei Li, Jiajun Wu (Stanford Vision Lab)

Columbia University, Columbia College

BA in Computer Science with concentration in Physics

Advisors: Daniel Hsu, Zoltan Haiman

• Research Lead for computer-use & browser-use agents, spearheading 3 first-author publications (7 total) on evaluations at top-tier conferences (ICLR, ICML '26); including ResearchRubrics, referenced in a Perplexity AI agent release.
• Collaborated with external organizations like the Center for AI Safety at Berkeley on benchmarks for economically useful tasks (Remote Labor Index) and tool-use (MCP-Atlas), referenced in OpenAI & Anthropic's latest model cards.
• Post-trained models with RL to analyze strategies for optimal data learning and contributed to research on an efficient RL algorithm with intermediate rewards (NeurIPS '25 workshop).
• Collaborated with researchers at top frontier labs, offering consultation on advanced computer-use data types involving programmatic & agentic verifiers.

• Spearheaded a project on LLM explainability & trust that was published at an ICML '24 workshop, & built an open-source LLM-testing framework (maite). Participated in a project collaboration with MIT Profs. Philip Isola & Jacob Andreas.
• Architected an internal tool for efficient evaluation of a Retrieval Augmented Generation (RAG) system with 15+ distinct metrics & assessed it on referencing past recordings in a speech-to-text application, achieving 92% accuracy.

• Engineered an end-to-end LiDAR 3D point-cloud classification system for autonomous vehicles, achieving >95% accuracy, ~2% FPR and 85% reduction in runtime. Deployed in Nissan Autonomous Vehicles beginning Winter '22.

• Ideated research projects on language models & diffusion models and attended various mentorship events as part of Google's CSRMP.

Stanford School of Engineering

Graduate Teaching Assistant

• Managed weekly Discussion Sections of 75+ students for some of the most popular CS courses at Stanford (>600 students); held office hours, constructed & graded HWs. Received >95% excellent reviews.
• Courses: CS 224N (NLP, Prof. Manning), CS 231N (Computer Vision, Prof. Fei-Fei Li), CS 230 (Deep Learning, Prof. Andrew Ng)
• Recorded the Python tutorial for Stanford's CS 224N course on YouTube

Columbia University, Department of Mathematics

Undergraduate Teaching Assistant for Calculus III (across 4 semesters)

• Graded assignments, led weekly office hours for ~150 students. Consistently received >80% excellent reviews.

Graduate Community Chair, Stanford Women in Computer Science

• Organized multiple events for the graduate community including mixers, industry alumni panels, lunches, talks, etc.; spearheaded a focus on MS students through the setup of an MS student alumni panel and care-package mixer.
• Managed over $3000 in funds from the CS department and Engineering schools to fund events for graduate students.