Virginia Smith

Virginia Smith

I'm an assistant professor in the Machine Learning Department at Carnegie Mellon University, and a courtesy faculty member in the Electrical and Computer Engineering Department. My 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.

Prior to CMU, I was a postdoc with Chris Ré at Stanford University. I received my PhD at UC Berkeley, where I worked with Michael I. Jordan and David Culler as a member of the AMPLab.

PhD Students




Progressive Compressed Records: Taking a Byte out of Deep Learning Data
M. Kuchnik, G. Amvrosiadis, V. Smith
Federated Learning: Challenges, Methods, and Future Directions
T. Li, A. K. Sahu, A. Talwalkar, V. Smith
Refereed Conference or Journal

Federated Optimization in Heterogeneous Networks
T. Li, A. K. Sahu, M. Sanjabi, M. Zaheer, A. Talwalkar, V. Smith
Conference on Machine Learning and Systems (MLSys), 2020
Fair Resource Allocation in Federated Learning
T. Li, M. Sanjabi, A. Beirami, V. Smith
International Conference on Learning Representations (ICLR), 2020
FedDANE: A Federated Newton-Type Method
T. Li, A. K. Sahu, M. Sanjabi, M. Zaheer, A. Talwalkar, V. Smith
Asilomar Conference on Signals, Systems and Computers, 2019, Invited Paper
A Kernel Theory of Modern Data Augmentation
T. Dao, A. Gu, A. Ratner, V. Smith, C. De Sa, C. Re
International Conference on Machine Learning (ICML), 2019
Efficient Augmentation via Data Subsampling
M. Kuchnik, V. Smith
International Conference on Learning Representations (ICLR), 2019
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
V. Smith, S. Forte, C. Ma, M. Takac, M. I. Jordan, M. Jaggi
Journal of Machine Learning Research (JMLR), 2018
Federated Multi-Task Learning
V. Smith, C. Chiang, M. Sanjabi, A. Talwalkar
Neural Information Processing Systems (NeurIPS), 2017
Distributed Optimization with Arbitrary Local Solvers
C. Ma, J. Konecny, M. Jaggi, V. Smith, M. I. Jordan, P. Richtarik, M. Takac
Optimization Methods and Software, 2017
Going In-Depth: Finding Longform on the Web
V. Smith, M. Connor, I. Stanton
Conference on Knowledge Discovery and Data Mining (KDD), 2015
Adding vs. Averaging in Distributed Primal-Dual Optimization
C. Ma*, V. Smith*, M. Jaggi, M. I. Jordan, P. Richtarik, M. Takac
International Conference on Machine Learning (ICML), 2015
Communication-Efficient Distributed Dual Coordinate Ascent
M. Jaggi*, V. Smith*, M. Takac, J. Terhorst, S. Krishnan, T. Hofmann, M. I. Jordan
Neural Information Processing Systems (NeurIPS), 2014
MLI: An API for User-friendly Distribued Machine Learning
E. Sparks, A. Talwalkar, V. Smith, X. Pan, J. Gonzalez, T. Kraska, M. I. Jordan, and M. J. Franklin
IEEE International Conference on Data Mining (ICDM), 2013
A Comparative Study of High Renewables Penetration Electricity Grids
J. Taneja, V. Smith, D. Culler, and C. Rosenberg
IEEE International Conference on Smart Grid Communications (SmartGridComm), 2013
Identifying Models of HVAC Systems Using Semiparametric Regression
A. Aswani, N. Master, J. Taneja, V. Smith, A. Krioukov, D. Culler, and C. Tomlin
Proceedings of the American Control Conference (ACC), 2012
Modeling Building Thermal Response to HVAC Zoning
V. Smith, T. Sookoor, and K. Whitehouse
ACM SIGBED Review, 2012
Workshop / Other

LEAF: A Benchmark for Federated Settings
S. Caldas, P. Wu, T. Li, J. Konecny, B. McMahan, V. Smith, A. Talwalkar
Workshop on Federated Learning for Data Privacy and Confidentiality at NeurIPS, 2019
MLSys: The New Frontier of Machine Learning Systems
One-Shot Federated Learning
N. Guha, A. Talwalkar, V. Smith
Machine Learning on Devices Workshop at NeurIPS, 2018
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework
V. Smith, S. Forte, M. I. Jordan, M. Jaggi
ML Systems Workshop at ICML, 2016
Classification of Sidewalks in Street View Images
V. Smith, J. Malik, and D. Culler
WiP Workshop at International Green Computing Conference (IGCC), 2013
MLbase: A Distributed Machine Learning Wrapper
A. Talwalkar, T. Kraska, R. Griffith, J. Duchi, J. Gonzalez, D. Britz, X. Pan, V. Smith, E. Sparks, A. Wibisono, M. J. Franklin, and M. I. Jordan
Big Learning Workshop at NeurIPS, 2012