United States
PhD candidate in Data Science at Boston University, interested in using artificial intelligence to advance scientific discovery. My research mainly focuses on design and analayis of interpretable clustering models, giving users meaningful understanding of the decision processes underlying their models. I am especially interested in applying these techniques to scientific challenges, and am currently studying environmental data from coastal ecosystems. Previously, I have worked on clustering and matrix factorization algorithms for studying the spatio-temporal patterns of Covid-19.
Algorithms for Data Science and BU's Research in Science and Engineering Program (RISE)
Conducted research at the intersection of artificial intelligence and scientific discovery, with a specific focus on the development of interpretable clustering algorithms. Applied interpretable models towards analysis for spread of Covid-19 and coastal ecosystem data, creating transparent insight for scientific applications.