Supervision and Collaborations

Ongoing work with

Past work

2024

• Louise Allain (M2 MVA) on statistical graph machine learning.
• Predoi Silviu-Adrian (M1 ENS Physics, Apr – Sep) on graph-based clustering.
• Tokinirina Hansen and Yorick Libiot (L3 ENS Maths, Apr – Jun) on random walk dynamics.

2023

• Jeanne Belly (ENS P.S. ARIA M1) on social network study of propagation phenomena.
• Teo Tessier (ENS P.S. ARIA M1) on graph generation methods.
• Perceval Beja-Battais (M2 MVA) work on learning theory, boosting, etc.
• Gaëtan Serré (M2 MVA) on global optimization.
• Gwendal Debaussart (M2 MVA) on statistical machine learning.
• Eloi Campagne (M2 MVA) on time-series analysis and prediction (colab. with EDF.
• Gaspard Abel (M2 ENS PS Physics) on information diffusion (co-supervision with Jean-Pierre Nadal, EHESS).
• Mohamed Aymen Bouyahia (M1 ENSTA Paris) on interpretable ML.
• Abdeljalil Zoubir (UM6P, visiting PhD student from Morocco, Apr – July) on graph neural networks with application to chemical compounds (co-supervision with Badr Missaoui (UM6P, Morocco).
• Manon Gouttefangeas and David Pieroucci (L3 ENS Maths, Apr – Jun) on paired sample hypothesis testing (co-supervision with I. Bargiotas and N. Vayatis).
• Quentin Boiret and Amer Essakine (L3 ENS Maths, Apr – Jun) on graph neural networks for time series.

2022

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

2021

• Gaspard Abel (M1 Economics, ENS Paris-Saclay) on competition in epidemic systems.
• Timothé Chambéry and Alexandre Surin (L3 Maths, ENS Paris-Saclay) on graph-theoretical problems.
• Frédéric Zheng (M1 Mines-Paristech) on ensemble learning.
• Martin Graive (M2 MVA) on epidemics in micro-communities.
• 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).
• Frederic Zheng on Boosting.

2020

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

2019

• Alexandro de la Concha on change point detection for graph data
• Anthea Mérida on ensemble methods and interpretable ML
• Amel Addala on a problem related to simulations of harmonic perturbations in EDF’s power network using Machine Learning. (supervision of M2 internship, 2019).
• Rafael Gromit and Blandine Galiay on sequential selection processes over graphs (L3, 2019).
• Vincent Laheurte and Ferdinand Campos will work on statistical homogeneity testing (L3, 2019).

2018

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

2017

• Marin Scalbert on modeling and mining from railway traffic data (BSc internship, 2017).
• Matteo Neri on studying the diffusive properties of perturbations in transportation networks (supervision of M2 internship, 2017).
• Mathilde Fekom on seeing the diffusion control in an online decision making framework (supervision of M2 internship, 2017).
• Otávio Leite Bastos de Nazaré on financial networks and default propagation (intern from ENSTA Paritech, 2017).

2016

• Luca Corinzia, on spectral methods for suppressing Information Cascades, (M2 internship, 2016).
• Stefano Sarao, on extensions of SIS model and LRIE strategy for social diffusion control, (M2 internship, 2016).
• Xavier Lioneton, on clustering twitter propagation data, (M2 internship, 2016).

2015

• Suzanne Schlich and Julie Tourniaire, on feature engineering and clustering of url progation profiles on twitter (L3 internship, 2015).
• Pierre Abgrall and Jules Baleyte, on systemic risk analysis in financial networks (L3, 2015).

2014

• Mateo Sesia, on network inference by observing SIS processes (M2 internship, 2014).
• Patrick Saux and David Marchand, on network inference (L3 internship, 2014).

(Co-)supervision at the CS Department at the University of Ioannina, 2010-2016

• “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).
• “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

• 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).