Assistant Professor Department of Statistics and Data Science Field member in the Department of Computer Science Email: elalaoui at cornell dot edu Office: 1180 Comstock Hall

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

Teaching

• 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).

A few recorded lectures

• 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.

Publications

• Hardness of sampling solutions from the symmetric binary perceptron.
  With D. Gamarnik.
  Preprint 2024. [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].
  

• Fast relaxation of the random field Ising dynamics.
  With R. Eldan, R. Gheissari, and A. Piana.
  Annals of Probability, to appear 2024+. [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, Vol. 47, no 19, 2024. [journal, 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].
  

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.