Sotabase

Career

· Professor, Electrical Engineering & Computer Sciences, University of California, Berkeley2013–
· Postdoctoral Researcher, Caltech2012–
· Assistant Professor, Computer Sciences, University of Wisconsin-Madison2009–2013
· Post-doctoral Fellow, California Institute of Technology2006–2009
· Research Assistant, Massachusetts Institute of Technology2000–2006
· Postdoctoral Fellow, Center for the Mathematics of Information, California Institute of Technology
· Master of Science, Massachusetts Institute of Technology
· PhD Student, Massachusetts Institute of Technology
· M.S. and PhD, MIT Media Laboratory
· Assistant Professor, University of California, Berkeley
· Bachelor of Science, University of Chicago

Publications (188)

Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics · 2008
6,200
cited
Understanding deep learning requires rethinking generalization
International Conference on Learning Representations · 2016
4,943
cited
Random Features for Large-Scale Kernel Machines
Neural Information Processing Systems · 2007
4,668
cited
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
SIAM Review · 2007
3,865
cited
Understanding deep learning (still) requires rethinking generalization
Communications of the ACM · 2021
2,559
cited
Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
Neural Information Processing Systems · 2011
2,338
cited
Plenoxels: Radiance Fields without Neural Networks
Computer Vision and Pattern Recognition · 2021
2,036
cited
Do ImageNet Classifiers Generalize to ImageNet?
International Conference on Machine Learning · 2019
2,015
cited
The Convex Geometry of Linear Inverse Problems
Foundations of Computational Mathematics · 2010
1,363
cited
Train faster, generalize better: Stability of stochastic gradient descent
International Conference on Machine Learning · 2015
1,355
cited
Compressed Sensing Off the Grid
IEEE Transactions on Information Theory · 2012
1,114
cited
The Marginal Value of Adaptive Gradient Methods in Machine Learning
Neural Information Processing Systems · 2017
1,114
cited
A Simpler Approach to Matrix Completion
Journal of machine learning research · 2009
1,061
cited
Tensor completion and low-n-rank tensor recovery via convex optimization
2011
819
cited
K-Planes: Explicit Radiance Fields in Space, Time, and Appearance
Computer Vision and Pattern Recognition · 2023
801
cited
A Tour of Reinforcement Learning: The View from Continuous Control
Annu. Rev. Control. Robotics Auton. Syst. · 2018
699
cited
Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints
SIAM Journal on Optimization · 2014
664
cited
Measuring Robustness to Natural Distribution Shifts in Image Classification
Neural Information Processing Systems · 2020
640
cited
Gradient Descent Only Converges to Minimizers
Annual Conference Computational Learning Theory · 2016
622
cited
Atomic Norm Denoising With Applications to Line Spectral Estimation
IEEE Transactions on Signal Processing · 2012
615
cited
Sotabase