I am an Applied Scientist at Amazon Web Services (AWS), where I work on data systems and semantic abstractions that help AI agents find, understand, and use data and organizational knowledge. More broadly, my research interests lie at the intersection of data systems and artificial intelligence, with a focus on building reliable, efficient, and usable infrastructure for AI applications.
I completed my PhD in Electrical Engineering and Computer Science in the Data Systems Group of the Computer Science and Artificial Intelligence Lab (CSAIL) at the Massachusetts Institute of Technology. During my time at MIT, I was advised by Prof. Tim Kraska and collaborated closely with Michael Cafarella and Prof. Samuel Madden. My doctoral research explored how machine learning and causal inference can be used to diagnose performance problems and automatically manage complex data systems.
Before MIT, I earned my Bachelor's of Science in Engineering (B.S.E.) in Electrical Engineering from Princeton University, alongside a certificate (minor) in Applications of Computing. For my undergraduate thesis, I had the honor of working with Prof. Margaret Martonosi on efficient memory consistency testing, as well as on formal verification for the DECADES project.