• Sep. 2026 : Marie Boyer stated her PhD, she is co-supervised by Xiaoyi Mai (Univ Toulouse Jean Jaures). Her PhD project is centered around bias amplification in statistical learning.

• June 2026 : Congratulations to Waïss Azizian for defending his stellar Ph.D. ! The jury included Francis Bach, John Duchi, Gabriel Peyré, Niao He, Anatoli Juditsky, Julien Mairal and his co-advisors: J. Malick, P. Mertikopoulos, and I.

• Apr. 2026 : Iyad Zekhnini started his PhD. He is co-supervised by Jérôme Malick and Tung Le (CNRS & LJK, Grenoble) and will work on stochastic optimization for non-convex machine learning objectives.

• Feb. 2026 : My Tremplin-ERC project DROPS was funded by the ANR (awarded since my ERC CoG proposal was rated A at step 2 but not funded).

• Dec. 2025 : Our project MADLearn, led by F. Malgouyres, was funded by the PEPR IA program.

• Jan. 2025 : I am now in charge of the Statistics and Optimization team of the lab.

• Dec. 2024 : FocusTech ANITI on skwdro. Here is the presentation and code zip

• Nov. 2024 : Our library skwdro now has an associated paper !

• Sep. 2024 : I am a co-PI of an ANITI (Toulouse's AI Cluster) chair on Trust and Responsibility in Artificial Intelligence led by JM. Loubes and J. Bolte

• Sep. 2024 : I am taking the responsibility of the 2nd year of the Master SID in Data Science & Engineering

• Aug. 2024 : Our library skwdro has a v1! We provide efficient code for machine learning with Wasserstein Distributional Robustness. The project is still very much under development but this version marks a first step in our objective of popularizing this approach. Available on Github and pip; paper coming soon !

• Jul. 2024 : Our project MAD (Maths of Automatic Differentiation) led by S. Vaiter (CNRS & U. Nice) is funded by the ANR !

• June 2024 : Yu-Guan's PhD thesis has been awarded the Université Grenoble Alpes's PhD award !

• Apr. 2024 : I am the happy father of Louise ! This also means I will be travelling less frequently in the coming months (maybe years?).

• Oct. 2023 : Congratulations to Yu-Guan Hsieh for his fanstastic Ph.D. ! The jury included Constantinos Daskalakis, Sylvain Sorin, Nicolò Cesa-Bianchi, Alexandre d’Aspremont, Anatoli Juditsky, Maryam Kamgarpour, and his co-advisors: J. Malick, P. Mertikopoulos, and I.

• Sep. 2023 : After seven great years in Grenoble, I have moved to Toulouse!

• Dec. 2022 : Congratulations to Gilles Bareilles for passing his Ph.D. with flying colors! The jury included Nadia Brauner, Jalal Fadili, Claudia Sagastizábal, Jean-Charles Gilbert, Mathurin Massias, Claude Lemaréchal and his co-advisors: J. Malick and I.

• Oct. 2022 : Waïss Azizian started his PhD. He is co-supervised by Panayotis Mertikopoulos and Jérôme Malick (CNRS & LJK, Grenoble) and will work on non-convex optimization for learning problems.

• Mar. 2022 : Two interns have joined the team: Waïss Azizian (student at ENS Paris for his third internship with us!) and Julien Prando (student at UGA). They will both work on regularization aspects of distributionally robust methods.

• Mar. 2022 : The second "Journée SMAI MAS-MODE" was a success: 7 talks and around 40 participants (in person!) at the intersection between optimization, probability, and statistics. website

• Feb. 2022 : Victor Mercklé started his PhD. He is co-supervised by Ievgen Redko (Univ. St-Etienne) and will work on congestion games in networks.

• Dec. 2021 : I defended my Habilitation à Diriger des Recherches (HDR) on Wed., December 15th! The jury was composed of Alexandre d'Aspremont (reviewer), Jérôme Bolte (reviewer), Adrian Lewis (reviewer), Jalal Fadili, Adeline Leclercq-Samson (president), and Julien Mairal. manuscript defense slides

• June 2021 : Waiss Azizian (Student at ENS Paris) visited again our team for six weeks as part of his (previously online) internship to work on distributionally robust methods.

• Nov. 2020 : Congrats to Dmitry “Mitya” Grishchenko for successfully defending his Ph.D.! The jury included Pascal Bianchi, Peter Richtarik, Julien Mairal, Samuel Vaiter, Alexander Gasnikov and his co-advisors: J. Malick, M. Amini, and I.

• June 2020 : Waiss Azizian (Student at ENS Paris) visited our team for six weeks as part of his (previously online) internship to work on extragradient methods.

• July 2019 : I was awarded an ANR JCJC grant (Young Research grant from the French Research Agency). The project is named STROLL: Harnessing Structure in Optimization for Large-scale Learning.

• June 2019 : I am part of the chair on Optimisation and Learning, lead by Jerome Malick and Yurii Nesterov, funded by the Grenoble AI Institute.

• June 2019 : Elnur Gasanov (Student at KAUST with P. Richtarik) visited our team for five weeks to work on distributed optimization.

• Mar. 2019 : Alexander Rogozin (Student at MIPT Moscow with A. Gasnikov) visited our team for two weeks to work on distributed structure optimization.

• Jan. 2019 : Mathias Chastan began his PhD between ST MicroElectronics and our team. He is co-supervised by A. Lam, J. Malick and I and will work on machine learning for industrial defects detection.

• W. Azizian, F. Iutzeler, J. Malick, P. Mertikopoulos : The global convergence time of stochastic gradient descent in non-convex landscapes: Sharp estimates via large deviations, International Conference on Machine Learning (ICML), 2025.
  

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• W. Azizian, F. Iutzeler, J. Malick, P. Mertikopoulos : What is the Long-Run Distribution of SGD? A Large Deviations Analysis, International Conference on Machine Learning (ICML), 2024.
  

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• W. Azizian, F. Iutzeler, J. Malick : Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models, NeurIPS, Dec. 2023. PDF

• C. Dapogny, F. Iutzeler, A. Meda, B. Thibert : Entropy-regularized Wasserstein distributionally robust shape and topology optimization, Structural and Multidisciplinary Optimization, vol. 66, art. 42, 2023. PDF

• W. Azizian, F. Iutzeler, J. Malick : Regularization for Wasserstein Distributionally Robust Optimization, ESAIM: Control, Optimisation, Calculus of Variations, 2023. PDF

• skwdro: Distributionally robust machine learning with Pytorch and Scikit-learn wrappers GitHub

• F. Vincent, W. Azizian, F. Iutzeler, J. Malick : skwdro: a library for Wasserstein distributionally robust machine learning, Journal of Machine Learning Research, vol. 27, no. 8, 2026.
  

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• June 2026 : Around distributional robustness and generalization in machine learning, Journée du département SI de l'IRIT, Toulouse (France)

• Apr. 2026 : A study of the long-run behavior of Stochastic Gradient Descent via large deviations, MAD-Stat TSE, Toulouse (France)

• Apr. 2025 : What is the long-run distribution of SGD?, JS2O, Perpignan (France)

• Mar. 2025 : What is the long-run distribution of SGD?, Colloquium de Mathématiques, Avignon (France)

• Jan. 2025 : What is the long-run distribution of SGD?, Séminaire Mathématiques de l’IA, Bordeaux (France)

• Nov. 2024 : What is the long-run distribution of SGD?, Workshop "Beyong classical regimes in statistical learning", Toulouse (France)

• June 2024 : Distributionally Robust Optimization and Statistical learning, Journée optimisation et controle, Pau (France)

• June 2024 : Distributionally Robust Optimization and Statistical learning, ENAC, Toulouse (France)

• Oct. 2023 : Distributionally Robust Optimization and Statistical learning, MADSTAT, Toulouse School of Economics (France) Slides

• Oct. 2023 : Distributionally Robust Optimization and Statistical learning, SPOT, Toulouse (France)

• Jan. 2023 : on Wasserstein Distributionally Robust Optimization, Séminaire Proba. Stat., Nice (France)

• Jan. 2023 : on Wasserstein Distributionally Robust Optimization, Séminaire Optimisation et Statistique, Toulouse (France)

• Jun. 2022 : Identification et exploitation de structure en apprentissage régularisé, Journées MODE, Limoges (France)

• May. 2022 : Identifying & Using Structure in Machine Learning, Rencontres Statistiques Lyonnaises, Lyon (France)

• Apr. 2022 : Identifying & Using Structure in Machine Learning, INRIA Maasai, Nice (France)

• Aug. 2021 : Harnessing Structure in Composite Optimization problems, IFIP TC7, Quito (Ecuador) virtual Slides

• June 2021 : Harnessing Structure in Regularized Empirical Risk Minimization, CAp virtual Slides

• Nov. 2020 : Nonsmooth regularizations in Machine Learning: structure of the solutions, identification, and applications, IMAG Montpellier virtual Slides

• Sep. 2020 : a Randomized Proximal Gradient Method with Structure-Adapted Sampling, Journées SMAI MODE virtual Slides

• Mar. 2020 : Harnessing Structure in Optimization for Machine Learning, Optimization for Machine Learning, CIRM (France) Slides

• Oct. 2018 : Distributed Learning with Sparse Communications and Structure Identification, Séminaire INRIA Magnet, Lille (France)

• Jul. 2018 : Distributed Learning with Sparse Communications and Structure Identification, International Symposium on Mathematical Programming (ISMP), Bordeaux (France)

• June 2018 : Distributed Learning with Sparse Communications and Structure Identification, Séminaire Polaris and D.A.T.A., Grenoble (France)

• May. 2018 : Distributed Learning with Sparse Communications and Structure Identification, Journées de Statistique, Saclay (France)

• Apr. 2017 : Monotonicity, Acceleration, Inertia, and the proximal gradient algorithm , Optimization and Statistical Learning, Les Houches (France) Slides

• Nov. 2016 : Gossip Algorithms: Tutorial and Recent advances , SMILE in Paris, Paris (France) Slides Part I Slides Part II

• Oct. 2016 : Modified fixed points iterations and applications to randomized and accelerated optimization algorithms , Workshop Cavalieri, Paris (France)

• Sep. 2016 : Practical acceleration for some optimization methods using relaxation and inertia, Seminaire d'Analyse non lineaire et Optimisation, Avignon (France)

• June 2016 : Practical acceleration for some optimization methods using relaxation and inertia, Seminaire Signal-Image de l'Insitut de Mathematiques de Bordeaux, Bordeaux (France)

• June 2016 : Practical accelerations for the alternating direction method of multipliers , PICOF Workshop , Autrans (France)

• May 2016 : Descente par coordonnéees stochastique dan l'algorithme du point fixe et application aux méthod d'optimisation , Congres d'Analyse Numerique (CANUM) , Obernai (France)

• Nov. 2015 : Relaxation and Inertia on the Proximal Point Algorithm , Titan Workshop , Grenoble (France)

• Nov. 2015 : Relaxation and Inertia on Fixed point algorithms , Journées EDP Rhone-Alpes-Auvergne (JERAA), Clermont-Ferrand (France)

• Mar. 2015 : Online Relaxation Method for Improving Linear Convergence Rates of the ADMM , Benelux meeting on Systems and Control, Lommel (Belgium)

• Aug. 2014 : Asynchronous Distributed Optimization , Journées MAS, Toulouse (France)

• May. 2014 : Distributed Optimization Techniques for Learning over Big Data , 2014 ESSEC/Centrale-Supélec Conference Bridging Worlds in Big Data, ESSEC CNIT Campus, La Défense Paris (France)

• Apr. 2014 : Distributed Asynchronous optimization using the ADMM, Large graphs and networks seminar, Université Catholique de Louvain-la-Neuve , ICTEAM institute, Louvain-La-Neuve (Belgium) Slides

• Jul. 2013 : Distributed Optimization using a Randomized Alternating Direction Method of Multipliers , Digicosme Research Day, Digiteo, Gif-sur-Yvette

• Nov. 2012 : Distributed Estimation of the Average Value in Wireless Sensor Networks , Alcatel-Lucent Chair Seminar, Supélec, Gif-sur-Yvette

• Apr. 2012 : Some useful results on Matrix Products for Signal Processing , Ph.D. Candidates Seminar, Telecom ParisTech, Paris

• Oct. 2011 : Distributed Maximal Value Estimation , Ph.D. Candidates Seminar, Telecom ParisTech, Paris

• Sep. 2011 : Estimation distribuée du maximum dans un réseau de capteurs, Colloque GRETSI, Bordeaux (France)