My main areas of interest are Database Systems and Data-Centric Machine Learning. I like working on data discovery and mining problems like minimizing data bias, improving data quality for machine learning, differential privacy systems, and query models. My current project involves selecting coresets for huge datasets in order to train ML models on the coresets with equivalent accuracy, lowering computational costs and enhancing speed. I’m also developing a solution that will allow for rapid aggregate queries over massive knowledge graphs with missing values. I also enjoy working on problems in number theory, group theory, probability, combinatronics, and graph theory with applications in computer science.
I graduated from the University of Washington in Seattle with a B.Sc. in Mathematics. I worked on a variety of software engineering projects during the course of my four years, from research papers to side projects. Maimon, a framework that aids in approximating acyclic schema discovery from relations using Multivalued Dependencies, has been one of my main projects. Additionally, I’ve worked with LightDB and other Visual Database Management Systems. The Caltech Tensorlab is another place I’ve worked as a research intern. There, I researched how effectively the present neural network architecture can understand the compositional nature of data and helped develop a new architecture called Tree Stack Memory Units.