Sotabase

Career

· Associate Professor, Massachusetts Institute of Technology (MIT)2019–
· Assistant Professor, Massachusetts Institute of Technology (MIT)2015–2019
· Postdoctoral Associate, MIT2013–2015
· Interim Engineering Intern, Qualcomm2011–2011
· Graduate Student Researcher, UC Berkeley2006–2012
· PhD, Electrical Engineering and Computer Sciences, UC Berkeley2006–2012
· MS, Mathematics, University of Illinois at Urbana-Champaign2005–2006
· BS, Electrical and Computer Engineering, University of Illinois at Urbana-Champaign2003–2006

Publications (136)

The Approximate Capacity of the Many-to-One and One-to-Many Gaussian Interference Channels
IEEE Transactions on Information Theory · 2008
333
cited
Efficiently Learning Ising Models on Arbitrary Graphs
Symposium on the Theory of Computing · 2014
212
cited
Mixing Time of Exponential Random Graphs
2008 49th Annual IEEE Symposium on Foundations of Computer Science · 2008
172
cited
Reconstruction of Markov Random Fields from Samples: Some Observations and Algorithms
SIAM journal on computing (Print) · 2007
164
cited
Feasibility of Interference Alignment for the MIMO Interference Channel
IEEE Transactions on Information Theory · 2013
122
cited
Feasibility of interference alignment for the MIMO interference channel: The symmetric square case
2011 IEEE Information Theory Workshop · 2011
122
cited
Information Theory of DNA Shotgun Sequencing
IEEE Transactions on Information Theory · 2012
120
cited
Reducibility and Computational Lower Bounds for Problems with Planted Sparse Structure
Annual Conference Computational Learning Theory · 2018
119
cited
Reducibility and Statistical-Computational Gaps from Secret Leakage
Annual Conference Computational Learning Theory · 2020
100
cited
Optimal assembly for high throughput shotgun sequencing
BMC Bioinformatics · 2013
95
cited
Statistical Query Algorithms and Low-Degree Tests Are Almost Equivalent
Annual Conference Computational Learning Theory · 2020
71
cited
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
Neural Information Processing Systems · 2020
68
cited
The staircase property: How hierarchical structure can guide deep learning
Neural Information Processing Systems · 2021
65
cited
A Latent Source Model for Online Collaborative Filtering
Neural Information Processing Systems · 2014
59
cited
Optimal Average-Case Reductions to Sparse PCA: From Weak Assumptions to Strong Hardness
Annual Conference Computational Learning Theory · 2019
54
cited
The Algorithmic Phase Transition of Random k-SAT for Low Degree Polynomials
IEEE Annual Symposium on Foundations of Computer Science · 2021
51
cited
Learning a Tree-Structured Ising Model in Order to Make Predictions
Annals of Statistics · 2016
48
cited
Geometry of the 3-user MIMO interference channel
Allerton Conference on Communication, Control, and Computing · 2011
45
cited
Sample Efficient Active Learning of Causal Trees
Neural Information Processing Systems · 2019
44
cited
Structure learning of antiferromagnetic Ising models
Neural Information Processing Systems · 2014
43
cited
Sotabase
Guy Bresler | Researcher Profile | Sotabase | Sotabase