Senior Researcher at Microsoft Research. Interpretability & LLMs.
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UC Berkeley Electrical Engineering & Computer Sciences (EECS)
Doctor of Philosophy - PhD, Computer Science
January 1, 2017 – January 1, 2022
University of Virginia
Bachelor’s Degree, Mathematics and Computer Science
January 1, 2014 – January 1, 2017
Microsoft
Senior Researcher
August 1, 2022 – Present
Redmond, Washington, United States
Paige
AI Research Scientist
June 1, 2021 – May 1, 2022
Amazon Web Services (AWS)
Computer Vision Research Intern
May 1, 2020 – August 1, 2020
Response 4 Life
Data Scientist
March 1, 2020 – May 1, 2020
Pacmed
Interpretable Machine Learning Intern
June 1, 2019 – August 1, 2019
The Randstad, Netherlands
Berkeley Artificial Intelligence Research
PhD Research in Interpretable Machine Learning
August 1, 2017 – May 1, 2022
Berkeley, California
Computer Vision Intern
June 1, 2017 – August 1, 2017
Menlo Park
University of Virginia
Machine Learning Research Assistant
August 1, 2016 – June 1, 2017
Howard Hughes Medical Institute
Machine Learning Research Intern
May 1, 2015 – August 1, 2016
University of Virginia
Computational Neuroscience Research Assistant
November 1, 2014 – July 1, 2016
Howard Hughes Medical Institute
Scientific Computing Research Intern
June 1, 2014 – August 1, 2014
Research Innovations Incorporated
Web Dev / Android Intern
May 1, 2013 – February 1, 2014
Cultural Fit Analysis
The candidate's background, spanning academic research, large tech companies, and startups (Paige, Pacmed, Response 4 Life), demonstrates adaptability and a broad perspective. Their focus on interpretable ML and real-world applications (e.g., COVID-19 severity prediction, cancer diagnosis) suggests a drive for impactful work, which aligns well with a culture valuing innovation and societal contribution. The breadth of experience indicates a willingness to tackle diverse challenges.
Soft Skills & Operational Fit
The candidate's extensive research background, including teaching experience, suggests strong analytical, problem-solving, and communication skills. Their work on interpretable ML and bias in models indicates a thoughtful and ethical approach to AI development. The diversity of projects and companies implies adaptability and a collaborative mindset, crucial for operational fit in dynamic environments.