Assistant Professor |

**Research interests:**
High-dimensional phenomena in statistics and probability theory.
Algorithms on random structures.
Statistical physics.

STSCI6940 Readings & Research in High Dimensional Statistics (SP24).

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).

*Sampling from the SK measure via algorithmic stochastic localization*. [Talk].

Workshop on Spin Glasses, SwissMAP Research Station in les Diablerets, 2022.*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.

*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].

*Shattering in pure spherical spin glasses*.

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.

Journal of Physics A: Mathematical and Theoretical (to appear). [arxiv].

*Bounds on the covariance matrix of the Sherrington-Kirkpatrick model*.

With J. Gaitonde.

Electronic Communications in Probability 2024, Vol. 29, paper no. 18, 1-13. [journal, 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].

(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.

*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.