Teaching & Supervision

Ongoing work

  • 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.

Past work

(Co-)supervision at ENS Paris-Saclay:

2019

  • 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).

2018

  • 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).

2017

  • 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).

2016

  • 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).

2015

  • 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).

2014

  • 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

  • TA for the Optimization course for the ENS Paris-Saclay M1 students of the Maths Department (given by Alain Trouvé) (Fall 2018).
  • TA for the Statistics couse 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).
  • TA for the MVA Master course (M2): Unsupervised Learning: From Big Data to low-dimensional representations (given by Rene Vidal) (Fall 2017).