Boxin Zhang is an accomplished professional with a strong background in research and software engineering. Experience includes roles as a Research and Software Engineering Intern at the Institute of High Energy Physics, a Software Engineer Intern at McKinsey & Company, and various positions as a Quantitative Researcher at HAP Capital and 佳期投资 (JQ Investments). Currently, Boxin Zhang serves as a Machine Learning Engineer at Meta, focusing on the inference pipeline for the Segment Anything Model. Academic credentials include a Bachelor's degree in Theoretical and Mathematical Physics and a Master's degree in Artificial Intelligence from Stanford University. Additionally, Boxin Zhang has contributed as a Data Science Intern at the Stanford Institute for Economic Policy Research and as a Contributing Writer for The Stanford Daily, among other roles in research and education.
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