2018 Hellman Fellow
Assistant Professor, Computer Science
UC San Diego
Project Title: A Declarative, High-Throughput, and Reproducible Model Selection Framework for Deep Neural Networks
Project Description: This project proposes a new data processing framework and software to make it easier, faster, and cheaper for data scientists to explore, select, and apply powerful machine learning models called deep neural networks for analyzing large and complex datasets. It will integrate and innovate upon fundamental mathematical and computational properties of such models with key software system design principles from database systems and distributed systems.
Research Lab Webpage: https://adalabucsd.github.io