Hi, bonjour, yeia

Welcome to my personal web-page. I am Argyris Kalogeratos (Αργύρης Καλογεράτος), I come from Patras, Greece. My background is on Computer Science and I have a PhD on Machine Learning (2013).

Click for short bio

Argyris Kalogeratos holds a PhD in Computer Science (2013) from the University of Ioannina, Greece, on Machine Learning and Information Technologies, under the supervision of Prof. Aristidis Likas. His expertise ranges from Machine Learning and Artificial Intelligence, to Complex Networks and Decision & Control. Currently, A.K. is a permanent researcher at the Borelli Research Center, ENS Paris-Saclay, and member of the Machine Learning and Massive Data Analysis group (MLMDA). He is the primary investigator of the Machine Learning on Graphs and Complex Systems research theme. His research work has been published in journals such as IEEE Transactions on Network Science and Engineering, Springer’s Knowledge and Information Systems, Elsevier’s Data and Knowledge Engineering, as well as conferences such as NeurIPS, ICML, AAAI, AISTATS, INFOCOM, CDC, and others. Being affiliated with the Industrial Data Analytics and Machine Learning Chair (IdAML) of ENS Paris-Saclay, he has been involved in several synergistic projects among Academia and Industry for problems related to multimedia mining, social network analysis and mining, computational epidemiology, marketing, transportation, communication systems, and bioinformatics.


Position: I am a permanent researcher at the Centre Giovanni Borelli (ex Centre de Mathématiques et de Leurs Applications – CMLA), Ecole Normale Supérieure Paris-Saclay (ENS Paris-Saclay), University Paris-Saclay, France, and member of the Machine Learning and Massive Data Analysis (MLMDA) research group (head: Nicolas Vayatis).

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

Research initiatives: I participate to the research activities of the ML Industrial Big Data Chair (C.Borelli team: Charles Truong, Harry Sevi, Argyris Kalogeratos, Mathilde Mougeot, Nicolas Vayatis — past: Julien Audiffren). Among our industrial partners, there are ATOSWordline, SNCF, CEA, Michelin, Bertin Tech, and Banque de France.

Past funding: My research position at CMLA has 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.

Centre Borelli in a sentence

It’s a unique and dynamic environment where the academic research is developed while being challenged in real practical and industrial applications.



Research on COVID-19 epicemic

A new web page was posted hosting results from our lab and resources from the research community.

Research opportunities for MVA-ers

Here are some of the subjects that the ML group (MLMDA) of Center Borelli offers for MVA Masters program internships (but not limited to that): link.

Doors open afternoon – 14 December 15h00-18h00: The members of the Graph Machine Learning team will be happy to welcome the interested students at our lab at that day. We can talk about
this subject and the research opportunities at Centre Borelli. Please contact Argyris Kalogeratos to express your interest and allocate a time slot.


  • TechReport – The preprint Online non-parametric change-point detection for heterogeneous data streams observed over graph nodes, co-authored with A. de la Concha and Nicolas Vayatis can be found at [arxiv page].
  • JMLR – The aricle “Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation“, co-authored by P. Humbert, B. Le Bars, L. Oudre, A. Kalogeratos, and N. Vayatis, has been accepted to the Journal of Machine Learning Research (JMLR), 2021. The paper comes with a python package that implements the proposed method. [pdf][bib][code]
  • TechReport – A new review paper “Epidemic Models for COVID-19 during the First Wave from February to May 2020: a Methodological Review“, co-authored by M. Garin, M. Limnios, A. Nicolaï, I. Bargiotas, O. Boulant, S.E. Chick, A. Dib, T. Evgeniou, M. Fekom, A. Kalogeratos, C. Labourdette, A. Ovchinnikov, R. Porcher, C. Pouchol, and N. Vayatis. This is an important collaborative study conducted by members or Centre Borelli and other collaborators and colleagues during the last period. [pdf]
  • Summer-school – Time has come for this year’s French-German Summer-school that I am gladly co-organizing with folks and colleagues from Academia and Industry.
  • New internsTwo new L3 students from ENS PS Maths, Timothé Chambéry and Alexandre Surin will be working with us on graph-theoretic problems. Moreover,
  • Frédéric Zheng (M1 Mines-Paristech) will work on ensemble learning, and Martin Graive (M2 MVA) will work on epidemics in micro-communities.
  • AISTATS 2021 – The paper “Offline detection of change-points in the mean for stationary graph signals”, co-authored with A. de la Concha and N. Vayatis will appear in the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021) – 455/1527 submissions were accepted (29.8% rate) [pdf][appendix][code][poster]
  • PLoS ONE – The paper “Revealing posturographic profile of patients with Parkinsonian syndromes through a novel hypothesis testing framework based on machine learning”, co-authored with I. Bargiotas, M. Limnios, P.–P. Vidal, D. Ricard, and N. Vayatis, was accepted to the PLoS ONE journal. [older tech report: pdf, and the final version will become available soon]
  • PhD defenses – Three of our students that I gladly collaborated for several years are defending their PhDs next days. Mathilde Fekom (21/1), Pierre Humbert (22/1), and Batiste Le Bars (29/1). Good luck with reaching the final line guys!!
  • TechReport – “Efficient stream-based Max-Min diversification with minimal failure rate” co-authored with Μ. Fekom [pdf]
  • New team memberIt’s a great pleasure that Harry Sevi joined the MLMDA research group. His postdoc position will be funded by the Chair AI and it will be great that we will have the opportunity to work closely together on subjects related to ML and Graphs, as well as on industrial applications.
  • Blog page – Here’s a new blog page with posts around and beyond technical matters that do matter.
  • TechReport – “Efficient stream-based Max-Min diversification with minimal failure rate” co-authored with Mathilde Fekom (under conference review) [pdf].
  • New internI welcome with pleasure our new intern: Ivan Conjeaud (M1 ENS Economics and Parcour AI) will work on competitive epidemics with application to panic buying, trend diffusion, etc.
  • Workshop – Proud co-organizers of the Digital French-German Summer School with Industry 2020! Several works of our team will be presented in this event!
  • TechReport – “Winning the competition: enhancing counter-contagion in SIS-like epidemic processes” co-authored with S. S. Mannelli [pdf]
  • TechReport – “Model family selection for classification using Neural Decision Trees” co-authored with A. Mérida and M. Mougeot [pdf]
  • TechReport – “Offline detection of change-points in the mean for stationary graph signals” co-authored with A. de la Concha and N. Vayatis [pdf]
  • Follow-up – The paper “Dynamic Epidemic Control via Sequential Resource Allocation” co-authored with M. Fekom and N. Vayatis, is the finalization of the previous work published at CDC 2019 and now is under journal review [pdf].
  • ICML 2020 – The paper “Learning the piece-wise constant graph structure of a varying Ising model” co-authored with B. Le Bars, P. Humbert, and N. Vayatis, will appear in the International Conference on Machine Learning 2020 [pdf: a previous version, the final will be out soon].

See all news...