Engineering Professor Receives Facebook Award in Privacy Learning and Statistics

Suhas Diggavi, a 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.

Facebook announced the research program in April at The Web Conference. UCLA is the only University of California campus selected among the 10 winners and 15 finalists out of 134 total proposals from across the globe. Diggavi received a $50,000 award to support his research on a machine learning technique, known as federated learning, which trains an algorithm across distributed edge devices housing local data that could be sensitive and need to be kept private.

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.

Earlier this year, Diggavi was one of two UCLA Samueli professors to receive the 2020 Amazon Research Award. He was also awarded the 2021 Guggenheim Fellowship and the 2019 Google Faculty Research Award.

Sara Hubbard contributed to this story.