Hi, bonjour, yeia

This is a personal professional page. I have a Computer Science background, a PhD in Machine Learning (2013), and I work on the intersection between Computer Science and Applied Mathematics.

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Argyris Kalogeratos comes from Patras Greece. He has BSc and MSc degrees in Computer Science from the University of Ioannina, Greece. He holds a PhD in Machine Learning and Information Technologies (2013, supervisor: Prof. Aristidis Likas), from the same Institution. Furthermore, he has been a research scientist in the Centre of Applied Mathematics, ENS Cachan, France (2013-2018), and since 2018 he is a staff researcher in the Centre Borelli, ENS Paris-Saclay, Université Paris-Saclay, France. His interests include Machine Learning, Statistics, Network Science, Decision & Control, and Computational Social Systems. He has been involved in several synergistic projects between Academia and Industry, for problems related to multimedia mining, social network analysis and mining, computational epidemiology, marketing, transportation, communication systems, and bioinformatics. Argyris likes multidisciplinary research and the fusion of ideas, concepts, viewpoints, originating from different domains.

Currently

Position: I am a permanent senior researcher at the Centre Giovanni Borelli1, Ecole Normale Supérieure Paris-Saclay (ENS Paris-Saclay), University Paris-Saclay, France, and member of the Learning and Information Processing Systems (LIPS) research group2.
1 ex Centre de Mathématiques et de Leurs Applications (CMLA).    
2 ex Machine Learning and Massive Data Analysis (MLMDA).

Research focus: Coordinator of the ML on Graphs research theme.

Research initiatives: I participate in the research activities of the Industrial Data Analytics and Machine Learning Chair. Among our industrial partners, there are SNCF, CEA, Michelin. Other Academia-Industry synergies I am part of: with RTE&SNCF and EDF.

Other initiatives: Since 2017, I (co-)organize the seminars of our team.

Past funding

My research position at CMLA had been funded in the past by the MORANE project (in collaboration with SNCF railway company) and the SODATECH project  (funded by the French state) for which I was the research coordinator and responsible for CMLA’s part. See more details at the “Projects” section.

 

Current PhD students:

  • Alejandro de la Concha Duarte (~ statistical machine learning on graphs)
  • Gaëtan Serré (~ global optimization)
  • Gwendal Debaussart (~ graph representation learning)
  • Eloi Campagne (~ time-series forecasting for the energy sector)
  • Gaspard Abel (~ machine learning on social interaction networks)

Centre Borelli in a sentence

CB is a unique and dynamic environment where the academic research is developed while being challenged in real applications.

Announcements

EDF Data Challenge

The EDF Data Challenge is still open for contributions on the problem of forecasting electricity load (consumption). Feel free to diffuse and contribute! This is a data challenge in which our student Eloi Campagne leads the organization team. A paper giving more technical details can be found here.

News

  • Workshop: A workshop dedicated to diffusion of opinions and disinformation will take place on the 4th of April 2025 at EHESS-Paris. Co-organization of CAMS and Centre Borelli.
  • MLMDA is dead – Long live LIPS: Our research team has be renamed to Learning and Information Processing Systems – LIPS. Read more details can be found here.
  • New interns:
    • Yutai Zhao (M2 MHPC P.S.) on stream diversification (Apr–Oct).
    • Mahdi Hadj Taieb (M1 ENSTA Paris) on modeling and learning from social network activity (Jun-Aug).
    • Itai Zehavi (M1 ENS P.S. ARIA) on large-scale electricity forecasting (Feb-Jul, in collaboration with EDF).
    • Yann Moinard and Nidhal Sellik (L3 ENS P.S. Maths) on particle-based global optimization schemes (Mar-Jun).
  • Code: The code of a prototype implementation of the GSM-Degroot model is now available here].
  • AISTATS 2025:  Two papers will appear at the 28th International Conference on Artificial Intelligence and Statistics (AISTATS).
    • Collaborative non-parametric two-sample testing, co-authored with A. de la Concha and N. Vayatis [pdf].
    • Stein Boltzmann Sampling: A Variational Approach for Global Optimization, co-authored with G. Serré, and N. Vayatis [pdf].
  • New intern:
    • Itai Zehavi (M1 ENS P.S. ARIA) on a graph signal processing validation approach for forecasting methods (Oct-Jan).
  • FMJH Post-doc funding: The Foundation has announced the call for post-doc funding 2024. Check a subject proposal entitled Predictive maintenance for networked systems. Applications are due to the 9th of December [link].
  • EDF Challenge: The EDF Data Challenge (our student Eloi Campagne leads the organization team) is still open for contributions on the problem of forecasting electricity load (consumption). Feel free to diffuse and contribute!
  • SETN2024: A new paper with the title LIPO+: Frugal Global Optimization for Lipschitz Functions, co-authored with G. Serré (PhD student, CB), P. Beja-Battais (PhD student, CB), S. Chirrane, and N. Vayatis, will appear in EETN Conference on Artificial Intelligence. [pdf]
  • W@ECML2024: A new paper with the title Leveraging Graph Neural Networks to Forecast Electricity Consumption, co-authored with Eloi Campagne (PhD student, EDF&CB), Yvenn Amara-Ouali, and Yannig Goude, will appear at the European Conference on Machine Learning – Workshop ML4SPS (with proceedings), 2024. [pdf]
  • JdS 2024: Eloi Campagne (PhD student, EDF&CB) presented his work on the Forecasting Net Load in France – EDF Challenge 2024, at the 55th Journées de Statistique (JdS 2024, SFdS), in May, Bordeaux, France. Joint work with Yvenn Amara-Ouali and Yannig Goude from EDF. [pdf]
  • New interns:
    • Louise Allain (M2 MVA, Apr–Oct) on non-parametric graph inference.
    • Silviu-Adrian Predoi (M1 ENS P.S. Physics) on node embeddings (Apr-Sep).
    • Tokinirina Hansen and Yorick Libiot (L3 ENS P.S. Maths) on random walks over graphs.
  • Preprint: A new priprint with the title Collaborative non-parametric two-sample testing, co-authored with A.de la Concha, and N. Vayatis, 2024. [pdf]
  • Preprint: A new priprint with the title Stein Boltzmann Sampling: A Variational Approach for Global Optimization, co-authored with G. Serré, and N. Vayatis, 2024. [pdf]
  • AISTATS 2024: The paper Online non-parametric likelihοod-ratio estimation by Pearson-divergence functional minimization. with A. de la Concha and N. Vayatis, will appear in the 27th International Conference on Artificial Intelligence and Statistics (AISTATS), 2024 [pdf].
  • Preprint: Significant update to the work Collaborative likelihood-ratio estimation over graphs, co-authored with A. de la Concha, and N. Vayatis, can be found at [pdf].
  • Preprint: The new preprint UniForCE: The unimodality forest method for clustering and estimation of the number of clusters, co-authored with G. Vardakas and A. Likas, can be found at [pdf][code].
  • Preprint: The new preprint Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization, co-authored with A. de la Concha and N. Vayatis, can be found at [pdf].
  • New interns:
    • Jeanne Belly (M1 ENS P.S. ARIA) on social network study of propagation phenomena.
    • Teo Tessier (M1 ENS P.S. ARIA) on graph generation methods.
  • New PhD student:
    Eloi Campagne will work on forecasting electricity consumption in a Cifre Phd with EDF. Co-supervised with Mathilde Mougeot from CB, Yvenn Amara-Ouali and Yannig Goude from EDF.

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