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).

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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.

Currently

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.

 

Announcements

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:

News

  • 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) [the tech. report here: pdf and the final version will become available soon]
  • 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].
  • ONADAP project  – a newly funded research project/collaboration that will try to produce practical solutions for hospital monitoring and decision making during a period such as the recent pandemic. Centre Borelli and the MLMDA team have central role in this project.
  • New interns – I welcome with great pleasure our new interns:
    • Randa Elmrabet Tarmach (M2 MVA) will work on unsupervised statistical learning;
    • Kais Cheikh (M1 ENSTA) will work on budget(ed) learning;
    • Mohamed El Khames BOUMAIZA (M1 ENSTA) will work on change-points detection in network streams;
    • Wendong Liang and Baptiste Loreau (L3 Maths ENS Paris-Saclay) will work on the intersection of topological data analysis and graph theory;
    • Martin Dhaussy (L3 Economics ENS Paris-Saclay) will work graph robustification;
    • Aaron MAMANN (M1)… on epidemic signal processing.
  • New PhD with scholarship – Our PhD project proposal with the title “Towards Interpretable and Versatile Machine Learning” for the candidate Anthea Mérida (supervised by M. Mougeot and me) was ranked 1st in the DIM MathInnov of Paris Region PhD 2020.
  • COVID-19 epidemic – A new web page was posted hosting results from our lab and resources from the research community.
  • TechReport – “Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation”. P. Humbert, B. Le Bars, L. Oudre, A. Kalogeratos, and N. Vayatis, Technical Report, Nov 2019. [pdf]Abstract
    In this paper, we consider the problem of learning a graph structure from multivariate signals, known as graph signals. Such signals are multivariate observations carrying measurements corresponding to the nodes of an unknown graph, which we desire to infer. They are assumed to enjoy a sparse representation in the graph spectral domain, a feature which is known to carry information related to the cluster structure of a graph. The signals are also assumed to behave smoothly with respect to the underlying graph structure. For the graph learning problem, we propose a new optimization program to learn the Laplacian of this graph and provide two algorithms to solve it, called IGL-3SR and FGL-3SR. Based on a 3-steps alternating procedure, both algorithms rely on standard minimization methods – such as manifold gradient descent or linear programming – and have lower complexity compared to state-of-the-art algorithms. While IGL-3SR ensures convergence, FGL-3SR acts as a relaxation and is significantly faster since its alternating process relies on multiple closed-form solutions. Both algorithms are evaluated on synthetic and real data. They are shown to perform as good or better than their competitors in terms of both numerical performance and scalability. Finally, we present a probabilistic interpretation of the optimization program as a Factor Analysis Model. Keywords: Graph learning, graph Laplacian, non-convex optimization, graph signal processing, sparse coding, clustering.
  • Centre Giovanni Borelli – Since the 1st of January 2020, the Centre de Mathématiques et de Leurs Applications (CMLA) of the ENS Paris-Saclay and the research team CognacG of the University Paris Centre have formed the Center Giovanni Borelli (Unité Mixte de Recherche : UMR 9010).

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