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个人简介
I’m a PhD Student at UC Berkeley advised by Joseph Gonzalez and Sanjit Seshia. My current work focuses on the use of Machine Learning for repetitive optimization found in software systems and in logistics. I’m particularly interested in the automatic detection and exploitation of approximate symmetries in the design of data-driven optimizers. I collaborate closely with Yuandong Tian at FAIR on this pursuit.
In 2019-2020, I served as a research assistant in the Computer Science department at Columbia University working under Ronghui Gu and Suman Jana. Specifically, my focus was in designing machine learning techniques for automating deductive program verification in hopes of scaling the benefits of formal methods to large scale software systems. I previously graduated with a BS from the same department in 2019 and graduated high school from Germantown Academy in the suburbs of Philadelphia.
In 2019-2020, I served as a research assistant in the Computer Science department at Columbia University working under Ronghui Gu and Suman Jana. Specifically, my focus was in designing machine learning techniques for automating deductive program verification in hopes of scaling the benefits of formal methods to large scale software systems. I previously graduated with a BS from the same department in 2019 and graduated high school from Germantown Academy in the suburbs of Philadelphia.
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