Carnegie Mellon University

Machine learning Professors Ameet Talwalkar and Virginia Smith portrayed with a background of Carnegie Mellon's Gates Hillman Center on the back

May 28, 2020

Smith and Talwalkar Earn Towards On-Device AI Research Award

By Roberto Iriondo

Byron Spice
  • Director of Media Relations
  • 412-268-9068

Virginia Smith and Ameet Talwalkar, assistant professors in the School of Computer Science's Machine Learning Department, have received a Towards On-Device AI research award from Facebook Research.

Smith's research interests are in machine learning, optimization and distributed systems. Specific topics include large-scale machine learning, distributed optimization, federated and on-device learning, multi-task learning and data augmentation.

Talwalkar's research interests are in the field of statistical machine learning. His current work seeks to democratize machine learning, with a focus on topics related to scalability, automation, fairness, and interpretability of learning algorithms and systems.

Facebook launched the Towards On-Device AI research awards in December 2019, with the goal to support the academic community in addressing fundamental challenges in on-device AI research and to accelerate the transition to a truly "smart" world where AI capabilities fill all technological devices and sensors.

Smith and Talwalkar won with their proposal, "Private federated learning: Differential privacy in heterogeneous networks." Facebook received over 160 proposals from more than 111 universities in 24 countries around the world. For a full list of awardees, please visit Facebook Research's website.

Before joining Carnegie Mellon Smith received her B.A. in Mathematics and Computer Science from the University of Virginia, and her M.S. and Ph.D. in Computer Science from the University of California Berkeley. Talwalkar received his B.S. from Yale University and his Ph.D. in Computer Science from New York University.

For More Information
Byron Spice | 412-268-9068 |  
Virginia Alvino Young | 412-268-8356 |