Resume (pdf, updated August 2026)

Industry Experience

Google | Seattle, WA
Senior Research Scientist | Oct 2021 – Present

• Product Leadership: Led research for cross-functional product launches. Key contributions include new research-based features in Google Cloud’s RAG Engine and Gemini Enterprise (up to +34% accuracy vs. DIY RAG; +10% LLM parsing quality; +8% NDCG for LLM re-ranking).

• Agentic Quality: Bridging research and product, I co-lead the evaluation strategy for Agentic Search.

• Technical Impact: Directed research improving LLM reasoning and factuality, resulting in multiple top-tier conference papers (NeurIPS, ICLR, NAACL). Broadly interested in Generative AI, including LLM factuality, retrieval and systems, multi-agent harnesses, and context optimization. Filed 2 patents.

• Research Management: Mentored 8+ Ph.D. interns and 18+ "20% project" contributors.

Facebook Reality Labs (FRL) | Redmond, WA
Visiting Research Scientist | Aug 2018 – Dec 2018

• Researched impacts of physical location in AR/VR within the Surreal Group on SLAM and Vision.

• Developed algorithms, built simulations, and implemented prototypes.

Microsoft Research (MSR & MSR AI) | Redmond, WA
Research Intern | Aug 2016 – Jan 2018 (Two internships)

• DNA Data Storage: Developed user-friendly packages for encoding/decoding data for DNA storage and improved clustering algorithms for Nanopore sequencing.

• Algorithms and Implementation: Designed new distributed edit distance clustering algorithms.

• Outcomes: Lead author on a NIPS 2017 Spotlight paper; contributed to Nature Biotechnology paper; filed multiple patents.

Cray, Inc. | Seattle, WA
Research Intern | Jun 2015 – Oct 2015

• Developed new algorithms for triangle counting and database queries for the Cray Graph Engine.

Academic Experience

University of California, San Diego (UCSD) | San Diego, CA
Data Science Fellow (Postdoc) | Jan 2019 – Oct 2021

• Collaborated with Professors Kamalika Chaudhuri, Sanjoy Dasgupta, and Paul H. Siegel.

• Adversarial robustness, explainable AI, and representation learning.

• Instructor for "Algorithms for Big Data" (2020) and "Data Science Through a Geometric Lens" (2019).

Education

University of Washington | Seattle, WA

Ph.D. in Computer Science and Engineering (May 2018)

• Dissertation: New Algorithmic Tools for Distributed Similarity Search and Edge Estimation. Advisor: Paul Beame.

M.S. in Computer Science and Engineering (Dec 2014)

• Thesis: Information Complexity for Multiparty Communication.

University of Illinois Urbana-Champaign | Urbana, IL

B.S. in Computer Science (Dec 2010)

Senior Thesis: Lexical Semantics for Text-based Image Retrieval.

Professional Service

• Area Chair/Reviewer/Committee: NeurIPS, ICML, ICLR, AISTATS, COLT, SODA, ESA.

• Organizer: UCSD Machine Learning Blog (Editor 2020-2022), Workshops at NeurIPS, ITA, STOC.