Guy Bresler
Education Officer for AI+D; Professor of EECS and in IDSS
Studies high-dimensional inference at the interface of information theory, statistics, and theoretical computer science, including how combinatorial structure affects computational tractability.
Guy Bresler's lab at MIT works on learning theory & optimization, signal processing & information theory and efficient training & inference. 14 papers since 2023, 10 at top venues, mostly COLT and STOC. 10 alumni, most often to Citadel and Google DeepMind. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 71%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 10
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 7 papers2025 · T1 5
- 4 papers2024 · T1 3
- 3 papers2023 · T1 2
T1 venuesOtherVenues: COLT, STOC, FOCS, NeurIPS
Research focuspaper titles that use each topic's words
- Efficient training & inference1 paper
- Learning theory & optimization0 papers
- Signal processing & information theory0 papers
- studies high-dimensional0 papers
- theoretical computer0 papers
Paper list
From the lab homepage.
20257 papers
- COLT (T1)Computational Equivalence of Spiked Covariance and Spiked Wigner Models via Gram-Schmidt Perturbation (opens in a new tab)
- Conference on Algorithmic Learning Theory (ALT), and IEEE Trans. Info. TheoryEfficient reductions between some statistical models (opens in a new tab)
- NeurIPS (T1)Global Minimizers of Sigmoid Contrastive Loss (opens in a new tab)
- STOC (T1)Near-Optimal Time-Sparsity Trade-Offs for Solving Noisy Linear Equations (opens in a new tab)
- COLT (T1)Partial and Exact Recovery of a Random Hypergraph from its Graph Projection (opens in a new tab)
- Random Structures & AlgorithmsRandom Algebraic Graphs and Their Convergence to Erdos-Renyi (opens in a new tab)
- STOC (T1)Sandwiching Random Geometric Graphs and Erdos-Renyi with Applications: Sharp Thresholds, Robust Testing, and Enumeration (opens in a new tab)
20244 papers
- COLT (T1)Detection of L_infinity Geometry in Random Geometric Graphs: Suboptimality of Triangles and Cluster Expansion (opens in a new tab)
- Annals of Applied ProbabilityMetastable Mixing of Markov Chains: Efficiently Sampling Low Temperature Exponential Random Graphs (opens in a new tab)
- STOC (T1)On The Fourier Coefficients of High-Dimensional Random Geometric Graphs (opens in a new tab)
- COLT (T1)Thresholds for Reconstruction of Random Hypergraphs From Graph Projections
20233 papers
- FOCS (T1)Algorithmic Decorrelation and Planted Clique in Dependent Random Graphs: The Case of Extra Triangles (opens in a new tab)
- COLT (T1)Detection-Recovery and Detection-Refutation Gaps via Reductions from Planted Clique (opens in a new tab)
- Random Structures and AlgorithmsThreshold for Detecting High Dimensional Geometry in Anisotropic Random Geometric Graphs (opens in a new tab)
Where alumni went10 listed, 29% to industry
Industry 2Academia 5
Most often to
- Citadel
- Google DeepMind
Also to
- EPFL
- Stanford University
Apply to MITFall 2027
MIT EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
- USD 90
Confirmed on the program page.
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