Industry Experiences

Machine Learning Engineer / Data Science Intern

[ May - Aug 2024, May - Aug 2025 ]

[ Gilead Sciences ] 

[ San Francisco, CA ]

Developed a mathematical network-based framework that integrates a wide range of Gilead’s data systems across diverse data types, enabling scalable queries, anomaly detection, and previously unachievable root cause analysis in QA.

Applied expertise in network theory to develop a flexible, adaptable, and robust framework for exploring and analyzing document data topology at both individual and system-wide levels, enabling deeper insights, improved data relationships, and enhanced scalability for complex dataset.

Topological Data Analysis, Independent Contractor

[March - Nov 2023 ]

[ Sonic Alchemy ] 

[ Remote ]

Provided specialized mathematical expertise and support to the CEO, contributing to the development of biofeedback data understanding and its ability to be represented in a topological network for analysis. 

Participated as a key backer in meetings, leveraging knowledge of geometry and topology to enhance the company's claims and strategies and future product plans.

Engaged in weekly seminar lectures organized by IPVIVE (Sonic Alchemy's sister company), staying updated on cutting-edge mathematical advancements and their potential impact on biofeedback research 

Machine Learning & Image Analysis Intern

[ June - Aug 2022 ]

[ Nine Square Therapeutics ]

[ San Francisco, CA ]

Built and launched imaging data pipeline using mathematical and machine learning tools to create holistic phenotypic profiles of  treatments and extracting meaningful insights from high-dimensional complex biological data sets.

Generated distance and success statistics (based off Kolmogorov Smirnoff, Mahalanobis) representative of treatment deviation interpretable to biologists with an statistical significance.

Designed software to integrate experimental design and biological quality control to ensure experimental robustness.