Assistant Professor Department of Statistics and Data Science Field member in the Department of Computer Science Email: elalaoui at cornell dot edu Office: 312 CIS building

Research interests: High-dimensional phenomena in statistics and probability theory. Algorithms for optimization, sampling and learning. Statistical physics.
My research is currently funded by the US National Science Foundation, Division of Mathematical Sciences, grant DMS 2450867.

Teaching

• STSCI6520 Statistical Computing I (F25).

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

• STSCI3080/5080 Probability Models and Inference (F21, SP22, F22, SP23, F23, F24, SP26).

• 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).

Students

• August Chen, 3rd year, Computer Science, co-advised with Karthik Sridharan.

• Haoxuan Fu, 2nd year, Statistics.

A few recorded lectures

• Hardness of approximate sampling in mean-field spin glasses. [Lecture].
  Towards a Theory for Typical-Case Algorithmic Hardness, Les Houches, 2025.

• 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

• On the discontinuous breaking of replica symmetry and shattering in mean-field spin glasses.
  With A. Auffinger and M. Sellke.
  Preprint 2025. [arxiv].
  

• Near-optimal shattering in the Ising pure p-spin and rarity of solutions returned by stable algorithms.
  Stochastic Processes and their Applications, 2025+. [arxiv].
  

• Fast relaxation of the random field Ising dynamics.
  With R. Eldan, R. Gheissari, and A. Piana.
  Annals of Probability, 2026. [arxiv].
  

• The Kikuchi hierarchy and tensor PCA.
  With A. Wein and C. Moore.
  Journal of the Association for Computing Machinery (JACM), 2025. [journal, arxiv].
  Presented at the 60th Annual Conference on Foundations of Computer Science (FOCS), 2019.
  

• Hardness of sampling solutions from the symmetric binary perceptron.
  With D. Gamarnik.
  Random Structures and Algorithms, volume 66, issue 4, 2025. [journal, arxiv].
  

• Sampling from mean-field Gibbs measures via diffusion processes.
  With A. Montanari and M. Sellke.
  Probability and Mathematical Physics, volume 6, issue 3, pages 961-1022, 2025. [journal, arxiv].
  

• Shattering in pure spherical spin glasses.
  With A. Montanari and M. Sellke.
  Communications in Mathematical Physics, volume 406, article number 111, 2025. [journal, 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, volume 47, article number 19, 2024. [journal, arxiv].
  

• Bounds on the covariance matrix of the Sherrington-Kirkpatrick model.
  With J. Gaitonde.
  Electronic Communications in Probability, volume 29, article number 18, 1-13, 2024. [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, volume 189, article number 27, 2022. [journal, arxiv].
  

• An Information-theoretic view of Stochastic Localization.
  With A. Montanari.
  IEEE Transactions on Information Theory, volume 68, article number 11, 7423-7426, 2022. [journal, arxiv].
  

• Optimization of mean-field spin glasses.
  With A. Montanari and M. Sellke.
  Annals of Probability, volume 49, article number 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.
  

• 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.