Virginia Smith

Courtesy Professor, Electrical and Computer Engineering

Bio

Virginia Smith's 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.

Research

Virginia Smith's research interests lie at the intersection of machine learning, optimization, and computer systems. A unifying theme of her research is to develop machine learning methods and theory that effectively leverage prior knowledge and account for practical constraints (e.g., hardware capabilities, network capacity, statistical structure). Specific topics include: distributed optimization, large-scale machine learning, resource-constrained learning, multi-task learning, transfer learning, and data augmentation.

Keywords

• Large-scale machine learning and distributed optimization
• Optimization framework for distributed machine learning (CoCoA), Apache Spark and Google’s TensorFlow