Suhas Diggavi, professor of electrical and computer engineering at the UCLA Samueli School of Engineering, has received a 2021 Facebook statistics for improving insights, models and decisions award for his research on privacy in machine learning and analytics.
Diggavi is the only professor in the University of California system to be selected among the 10 winners and 15 finalists for the award. Diggavi received a $50,000 award to support his research on federated learning, a machine learning technique which trains an algorithm across distributed edge devices housing local data that could be sensitive.
Specifically, Diggavi’s winning research looks at privacy in personalized learning models. Most personalized technologies require multiple devices to analyze large amounts of data, which can jeopardize individuals’ privacy in the process. Diggavi hopes to find a way to ensure robust protection of sensitive data while also advancing individualized models. This award will help support a larger program in his lab focused on private, secure and efficient distributed learning.
Diggavi leads the information theory and systems laboratory at UCLA, where his research focuses on how information theory can be used across a multitude of fields including learning, security, privacy, cyber-physical systems, wireless networks, bioinformatics and neuroscience.