Teaching & Supervision

Supervision

with Alejandro de la Concha on multivariate signals and graph signals segmentation (supervision of MSc thesis).
with Anthea Mérida on ensemble methods (supervision of MSc thesis).
with Ioannis Bargiotas and Myrto Limnios on statistical applications for biomedical engineering (collaboration, 2018-…).
with Mathilde Fekom on seeing the diffusion control in an online learning framework (supervision of PhD thesis, 2017-…).
with Batiste Le Bars on detection problems on networks (supervision of PhD thesis, 2017-…).
collaboration with Sergio Pegnier on Transfer Learning problems.
collaboration with Kevin Scaman (Microsoft Research, INRIA Paris-Saclay, France). We work on network diffusion models with application on large social graphs.

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).

“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).
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).
TD for the Hyper Performance Computing Master course (M2): Data and Machine Learning (given by Nicolas Vayatis) (Fall 2018, Fall 2019).
TA for the MVA Master course (M2): Unsupervised Learning: From Big Data to low-dimensional representations (given by Rene Vidal) (Fall 2017).