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.
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
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.
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.