Since October 2022, I am a postdoctoral researcher at EPFL in BAN chair led by Prof. Negar Kiyavash. Prior to that, I was a PhD student at Inria Paris in the Dyogene team, under the supervision of Laurent Massoulié and Marc Lelarge. Here is a short CV.
I am currently working on:
- Statistical inference in graphs and matrices
- Informational and computational thresholds for algorithms on random instances
- Optimal transport for statistical learning
- Bayesian networks, causality
Email adress: luca [dot] ganassali [at] epfl [dot] ch
Physical adress: Office 316, Building ODY, EPFL, Lausanne.
S. Akbari, L. Ganassali, N. Kiyavash. Learning causal graphs via monotone triangular transport maps, 2023, submitted.
[arXiv]
L. Ganassali, L. Massoulié, G. Semerjian. Statistical limits of correlation detection in trees, 2022, submitted.
[arXiv]
L. Ganassali. The graph alignment problem: fundamental limits and efficient algorithms, PhD dissertation, 2022. [pdf]
L. Ganassali, M. Lelarge, L. Massoulié. Correlation detection in trees for partial graph alignment, 2021, Innovations in Theoretical Computer Science (ITCS 2022).
[arXiv] [ITCS (extended abstract)]
L. Ganassali, M. Lelarge, L. Massoulié. Impossibility of Partial Recovery in the Graph Alignment Problem, 2021, in Proceedings of Thirty Fourth Conference on Learning Theory (COLT 2021).
[arXiv] [PMLR] [COLT presentation]
L. Ganassali. Sharp threshold for alignment of graph databases with Gaussian weights, 2020, Mathematical and Scientific Machine Learning (MSML21).
[arXiv] [PMLR] [MSML presentation]
M. Akian, L. Ganassali, S. Gaubert, L. Massoulié. Probabilistic and mean-field model of COVID-19 epidemics with user mobility and contact tracing, 2020, preprint.
[arXiv]
L. Ganassali, L. Massoulié. From tree matching to sparse graph alignment, 2020, in Proceedings of Thirty Third Conference on Learning Theory (COLT 2020).
[arXiv] [PMLR] [COLT presentation]
L. Ganassali, M. Lelarge, L. Massoulié. Spectral alignment of correlated Gaussian random matrices, 2019, in Advances in Applied Probability.
[arXiv] [journal]
21st INFORMS Applied Probability Society Conference, Nancy, June 28-30, 2023. (talk)
Séminaire de Probabilités, ENS/Université de Lyon 1, Lyon, May 4, 2023. (talk)
Young European Probabilists (YEP) workshop, Eindhoven, Mar. 27-31, 2023. (talk)
Séminaire de Probabilités/Statistiques, Institut de mathématique d'Orsay, Orsay, Jan. 12, 2023. (talk)
Séminaire de Probabilités, Centre de Mathématiques et Informatique, Université d'Aix-Marseille, Marseille, Nov. 15, 2022. (talk)
Theoretical Computer Science Spring School: Machine Learning, CIRM, Luminy, May 23-27, 2022.
CDM Seminar, EPFL, Mar. 17, 2022. (talk)
DACO Seminar, ETH Zürich, Feb. 28-Mar. 1, 2022. (talk)
Innovations in Theoretical Computer Science (ITCS), Berkeley (remote), Jan. 31-Feb. 3, 2022. (talk)
Stochastics Seminar, Georgia Tech (remote), Dec. 9, 2021. (talk)
Prairie Workshop, Paris, Nov. 10, 2021. (poster)
Colloque Jeunes Probabilistes et Statisticien-ne-s, Saint Pierre D'Oléron, Oct. 24-29, 2021. (talk, slides)
Workshop On Future Synergies for Stochastic and Learning Algorithms, CIRM, Luminy, Sept. 27-Oct. 1, 2021. (poster)
Junior conference Random networks and interacting particle systems (remote), Sept. 6-10, 2021. (talk)
Conference on Learning Theory (COLT) (remote), Aug. 15-19, 2021. (talk, slides, poster)
Mathematical and Scientific Machine Learning (MSML) (remote), Aug. 16-19, 2021. (talk)
Conference on Learning Theory (COLT) (remote), Jul. 9-12, 2020. (talk)
Workshop Spectra, Algorithms and Random Walks on Random Networks, CIRM, Luminy, Jan. 13-17, 2020.
Networking days, Orsay, Oct. 23, 2019. (talk)
Conferences: ISIT 2021, COLT 2022, MSML 2022.
Journals: Journal of Machine Learning Research, Annals of Statistics.
Spring 2020: Tutoring for the MAP361 course at Ecole Polytechnique, some revision exercices (in french) about
Borel-Cantelli lemma, Convergence of random variables,
and Estimation and CLT.
Spring 2021: TA for the MA16Y020 course: Statistiques et simulations probabilistes, L3, Université de Paris.
Spring 2021: TA for the MA1BY020 course: Statistiques, M1, Université de Paris.
Fall 2021: TA for the MT15Y030 course: Probabilités, L3, Université de Paris. Vous trouverez ici des exercices supplémentaires.
Spring 2022: TA for the MA1BY020 course: Statistiques, M1, Université de Paris. Voir les TPs.