Teaching & Supervision

Supervision and Collaborations

Ongoing work

  • with Harry Sevi and Matthieu Jonckheere on spectral clustering.
  • with Charles Truong in the frame of the IDAML AI Chair.
  • with Alejandro de la Concha on multivariate signals and graph signals segmentation (co-supervision of PhD thesis with Nicolas Vayatis).
  • with Anthea Mérida on ensemble methods (co-supervision of PhD thesis with Mathilde Mougeot).
  • with Ioannis Bargiotas on statistical applications for biomedical engineering (collaboration, 2018-…).
  • with Ivan Conjeaud and Philipp Lorenz-Spreen (Max Plank Institute, Germany) on opinion formation and dynamics, polarization and competitive epidemics.

Past work

(Co-)supervision at ENS Paris-Saclay:


  • with Jules Tsukahara (M2 MVA) on graph representation learning.
  • with Florian Châtel (L3 Maths, ENS Paris-Saclay) on interpretable ML.
  • with Antonin Casel and Ilyes Belkacem (L3 Maths, ENS Paris-Saclay) on unsupervised learning on graphs.
  • with Gaspard Abel on competitive diffusion processes (co-supervision of M1 internship at UCL).
  • with Ivan Conjeaud (co-supervision of M2 internship at PSE) on a game theoretic problem on graphs.
  • with Haytham Borchani (M1 ENSTA Paris) on interpretable ML.


  • with Gaspard Abel (M1 Economics, ENS Paris-Saclay) on competition in epidemic systems.
  • with Timothé Chambéry and Alexandre Surin (L3 Maths, ENS Paris-Saclay) on graph-theoretical problems.
  • with Frédéric Zheng (M1 Mines-Paristech) on ensemble learning.
  • with Martin Graive (M2 MVA) on epidemics in micro-communities.
  • with Ivan Conjeaud (M1 Economics, ENS Paris-Saclay) on the second phase of the work on opinion formation and dynamics, etc. (M1 internship, collaboration with Max Plank Institute).
  • with Frederic Zheng on Boosting.


  • with Baptiste Loreau and Wendong Liang (Maths Paris-Saclay) on Topological Data Analysis and possible applications on graphs (L3 internship).
  • with Randa El Mrabet-Tarmach (Master MVA) on data clustering. (MVA M2 internship).
  • with Kais Cheikh (ENSTA Paris) on budgeted learning. (M1 internship).
  • with Mohamed El Khames (ENSTA Paris) on segmentation of graph streams. (M1 internship).
  • with Ivan Conjeaud (Dpt Economics, ENS Paris-Saclay) on opinion formation and dynamics, polarization and competitive epidemics. (M1 internship).
  • with Martin Dhaussy (Dpt Economics, ENS Paris-Saclay) on a COVID-19 related project, epidemic control. (L3 internship).
  • with Thibaut Germain on a COVID-19 related project, generation of realistic contact networks. (volunteer collaborator).


  • with Alexandro de la Concha on change point detection for graph data
  • with Anthea Mérida on ensemble methods and interpretable ML
  • with Amel Addala on a problem related to simulations of harmonic perturbations in EDF’s power network using Machine Learning. (supervision of MSc thesis, 2019).
  • with Rafael Gromit and Blandine Galiay on sequential selection processes over graphs (Licence thesis, 2019).
  • with Vincent Laheurte and Ferdinand Campos will work on statistical homogeneity testing (Licence thesis, 2019).


  • with Antoine Prouff and Antoine Bordas on greedy dynamic resource allocationstrategies (L3 internship, 2018).
  • with Emile Ciuperca and Mathieu Helfter on graph signals and graph kernels (L3 internship, 2018).
  • with Thales Loiola Raveli will be working on the modeling of transportation traffic (intern from ENSTA Paritech, 2018).


  • with Marin Scalbert on modeling and mining from railway traffic data (BSc internship, 2017).
  • with Matteo Neri on studying the diffusive properties of perturbations in transportation networks (supervision of MSc thesis, 2017).
  • with Mathilde Fekom on seeing the diffusion control in an online learning framework (supervision of MSc thesis, 2017).
  • with Otávio Leite Bastos de Nazaré on financial networks and default propagation (intern from ENSTA Paritech, 2017).


  • with Luca Corinzia, on spectral methods for suppressing Information Cascades, (MSc thesis, 2016).
  • with Stefano Sarao, on extensions of SIS model and LRIE strategy for social diffusion control, (MSc thesis, 2016).
  • with Xavier Lioneton, on clustering twitter propagation data, (MSc thesis, 2016).


  • with Suzanne Schlich and Julie Tourniaire, on feature engineering and clustering of url progation profiles on twitter (Licence thesis, 2015).
  • with Pierre Abgrall and Jules Baleyte, on systemic risk analysis in financial networks (Licence thesis, 2015).


  • with Mateo Sesia, on network inference by observing SIS processes (MSc thesis, 2014).
  • with Patrick Saux and David Marchand, on network inference (Licence thesis, 2014).

(Co-)supervision at the CS Department at the University of Ioannina:

  • Document Classification using Advanced Representations”, Eirini Niaka, (Undergraduate thesis, 2011-2012).
  • Knowledge extraction from text collections using semantic information”, Nasos Tsamis (Undergraduate thesis, 2010-2011).
  • Event detection in text streams”, Panagiotis Zagorisios (Undergraduate thesis, 2012-2013 – MSc thesis, 2014-2015). This work led to the paper: “Improving Text Stream Clustering using Term Burstiness and Co-burstiness”, A. Kalogeratos, P. Zagorisios, and A. Likas, SETN, 2016. [pdf][slides][bib]
  • Information system for the rapid automatic identification of natural products in crude extracts based on NMR data“, Katerina Nikolaidi (Undergraduate thesis, 2011-2012). Ack: In collaboration with Lecturer Α. Tzakos and postdoctoral researcher V. Kontogianni, Department of Chemistry University of Ioannina.


  • Teaching of  the module AI/ML for network modeling – part of the scientific training of multidisciplinary students on AI (Spring 2020, Fall 2020).
  • TA for the Optimization course for the ENS Paris-Saclay M1 students of the Maths Department (given by Alain Trouvé) (Spring 2019, Spring 2020).
  • TA for the Statistics course of the Agregation Preparatoire (Spring 2019).
  • TA for the Introduction to CS Tools courses for the ENS Paris-Saclay L3 students of the Maths Department (Fall 2018, Fall 2019, Fall 2020).
  • TD for the Hyper Performance Computing Master course (M2): Data and Machine Learning (given by Nicolas Vayatis) (Fall 2018, Fall 2019, Fall 2020).
  • TA for the MVA Master course (M2): Unsupervised Learning: From Big Data to low-dimensional representations (given by Rene Vidal) (Fall 2017).