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Assistant Professor |
Research interests: High-dimensional phenomena in statistics and probability theory. Algorithms on random structures. Statistical physics.
Teaching
STSCI3080/5080 Probability Models and Inference (F21, SP22, F22, SP23, F23).
STSCI6940 Topics in High-Dimensional Inference (SP21).
STAT210B Theoretical Statistics, part B (TA, UC Berkeley, SP17).
CS174 Combinatorics and Discrete Probability (TA, UC Berkeley, SP15).
Some recorded lectures
Methods from statistical physics. [Lecture I, Lecture II, Lecture III].
Deep Learning Theory Workshop and Summer School, Simons Institute for the Theory of Computing, UC Berkeley, 2022.
Computing extremal cuts in locally treelike graphs. [Talk].
Groups and Dynamics Seminar, UT Austin, 2022.Optimization of mean-field spin glass Hamiltonians [Talk].
Math and Data Seminar, Courant Institute, NYU, 2021.
Publications
Fast relaxation of the random field Ising dynamics.
With R. Eldan, R. Gheissari, and A. Piana.
Preprint 2023. [arxiv].
Sampling from Mean-Field Gibbs Measures via Diffusion Processes.
With A. Montanari and M. Sellke.
Preprint 2023. [arxiv].
On the atypical solutions of the Symmetric Binary Perceptron.
With D. Barbier, F. Krzakala and L. Zdeborová.
Preprint 2023. [arxiv].
Shattering in pure spherical spin glasses.
With A. Montanari and M. Sellke.
Preprint 2023. [arxiv].
Bounds on the covariance matrix of the Sherrington-Kirkpatrick model.
With J. Gaitonde.
Preprint 2022. [arxiv].
Local algorithms for Maximum Cut and Minimum Bisection on locally treelike regular graphs of large degree.
With A. Montanari and M. Sellke.
Random Structures and Algorithms, 1-27, 2023. [journal, arxiv].
The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statistics.
With A. Bandeira, S. Hopkins, T. Schramm, A. Wein and I. Zadik.
Neural Information Processing Systems (NeurIPS) 2022. With oral presentation. [proc., arxiv].
Sampling from the Sherrington-Kirkpatrick Gibbs measure via algorithmic stochastic localization.
With A. Montanari and M. Sellke.
63th Annual Conference on Foundations of Computer Science (FOCS) 2022. [proc., arxiv].
Algorithmic pure states for the negative spherical perceptron.
With M. Sellke.
Journal of Statistical Physics, Vol. 189, Article number 27, 2022. [journal, arxiv].
An Information-theoretic view of Stochastic Localization.
With A. Montanari.
IEEE Transactions on Information Theory, vol. 68, no. 11, 7423-7426, 2022. [journal, arxiv].
Optimization of mean-field spin glasses.
With A. Montanari and M. Sellke.
Annals of Probability, Vol. 49, No. 6, 2922-2960, 2021. [journal, arxiv].
On the computational tractability of statistical estimation on amenable graphs.
With A. Montanari.
Probability Theory and Related Fields, 181, 815–864, 2021. [journal, arxiv].
Efficient Z_2 synchronization on Z^d under symmetry-preserving side information.
Preprint 2021. [arxiv].
Algorithmic thresholds in mean-field spin glasses.
With A. Montanari.
Preprint 2020. [arxiv].
Imputation for high-dimensional linear regression.
With K. Chandrasekher and A. Montanari.
Preprint 2020. [arxiv].
Fundamental limits of detection in the spiked Wigner model.
With F. Krzakala and M. I. Jordan.
Annals of Statistics, Vol 48, No. 2, 863-885, 2020. [journal, arxiv].
Based on an [earlier manuscript] (unpublished) containing additional results about finite-size corrections.
The Kikuchi hierarchy and tensor PCA.
With A. Wein and C. Moore.
60th Annual Conference on Foundations of Computer Science (FOCS) 2019. [arxiv].
Detection limits in the high-dimensional spiked rectangular model.
With M. I. Jordan.
31th Annual Conference on Learning Theory (COLT), PMLR 75:410-438, 2018. [proc., arxiv].
Decoding from pooled data: Sharp information-theoretic bounds.
With A. Ramdas, F. Krzakala, L. Zdeborová, M. I. Jordan.
SIAM Journal on Mathematics of Data Science 1-1 (2019), pp. 161-188. [journal, arxiv].
Decoding from pooled data: Phase transitions of message passing.
With A. Ramdas, F. Krzakala, L. Zdeborová, M. I. Jordan.
IEEE Transactions on Information Theory, 65, 572-585, 2019. [journal, arxiv].
Presented at IEEE International Symposium on Information Theory (ISIT) 2017. [proc.].
Tight query complexity lower bounds for PCA via finite sample deformed Wigner law.
With M. Simchowitz and B. Recht.
50th Annual Symposium on the Theory of Computing (STOC) 2018. [proc., arxiv].
Here’s an earlier version (not intended for publication) with slightly suboptimal results.
Estimation in the spiked Wigner model: A short proof of the replica formula.
With F. Krzakala.
IEEE International Symposium on Information Theory (ISIT) 2018. [proc., arxiv].
Asymptotic behavior of Lp-based Laplacian regularization in semi-supervised learning.
With X. Cheng, A. Ramdas, M. J. Wainwright, M. I. Jordan.
29th Annual Conference on Learning Theory (COLT), PMLR 49:879-906, 2016. [proc., arxiv].
Fast randomized kernel ridge regression with statistical guarantees.
With M. Mahoney.
Advances in Neural Information Processing Systems (NIPS) 28, 2015. [proc., arxiv].
CV
(2021-) Assistant professor, Department of Statistics and Data Science, Cornell University.
(Fall 2020) Richard M. Karp Research Fellow, Simons Institute for the Theory of Computing, UC Berkeley.
(2018-2020) Postdoctoral researcher, Stanford University. Hosted by Andrea Montanari.
(2013-2018) Ph.D. Electrical Engineering and Computer Sciences, UC Berkeley. Advised by Michael I. Jordan.
(2012-2013) M.Sc. Mathématiques, Vision et Apprentissage, Ecole Normale Supérieure/Ecole des Ponts Paristech.
(2009-2012) Diplome d'ingenieur, Applied math, Ecole Polytechnique.
Ph.D. Thesis
Detection limits and fluctuation results in some spiked random matrix models and pooling of discrete data. [link].
Ahmed El Alaoui.
Ph.D. Thesis, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, 2018.