Virginia Smith is an assistant professor in the Machine Learning Department at Carnegie Mellon University, and an affiliated faculty member in the Department of Electrical and Computer Engineering. Her research interests include machine learning, optimization, and distributed systems. Prior to CMU, Virginia received a Ph.D. from UC Berkeley and undergraduate degrees from the University of Virginia.

2217 Collaborative Innovation Center
Google Scholar
Virginia Smith
Virginia Smith’s website


2017 MS, Computer Science, University of California, Berkeley

2017 Ph.D., Computer Science, University of California, Berkeley

2012 BA, Computer Science, University of Virginia

2012 BA, Mathematics, University of Virginia


Media mentions

CyLab Security and Privacy Institute

CyLab awards 2024 seed funding

This year, CyLab has awarded $400K in seed funding to 17 CMU students, faculty, and staff members representing five departments at the university.

Carnegie Bosch Institute

Meet CBI Faculty Hosts: Virginia Smith and Steven Wu

Virginia Smith and Steven Wu actively collaborate in the area of federated learning, creating provable and deployable architectures to enable privacy-preserving machine learning across distributed data silos. Lack of privacy can be a bottleneck for adoption of future machine learning systems. Research towards privacy enhancing technologies is the central focus of the work of CBI fellow Pratiksha Thaker with Smith and Wu.

Engineering and Public Policy

SCS faculty, Ph.D. student named to MIT Technology Review’s 2021 Innovators Under 35 list

Virginia Smith, an assistant professor in the Machine Learning Department, and Priya Donti, a Ph.D. candidate in the Computer Science and Engineering and Public Policy departments, have been named to MIT Technology Review’s prestigious annual list of Innovators Under 35.

CMU Engineering

Six things you should know about AI from experts in the field

Researchers from Carnegie Mellon University’s College of Engineering share what they have learned about artificial intelligence while working in the field.