

Supervision, Scientific dissemination and teaching
Sections:

Recent activity (≥ 2017)

Former activity (≥ 2017)
Recent activity (≥ 2017)
Teaching (last update: February 2024)
202324: Machine Learning (8 hours). M1 students of the Toulouse Graduate School of Earth and Space Science (EUR TESS).
201920, 202324: Machine Learning (30 hours / year). International master course of Ecole Polytechnique (Palaiseau, France) given at Mohammed VI Polytechnic University (Ben Guerir, Morocco).
201819 to 202324: Statistical foundations of Machine Learning (15 hours / year). Master 2 degree at ISAE/Supaero (github).
201718 to 202324: Image Analysis (36 hours / year until 2019, then 16 hours / year). Master 2 in Mathematical Engineering. Université Paul Sabatier.
201718 to 202324: GPGPU computing with CUDA and OpenCL (6 hours / year). Master 2 degree at ISAE/Supaero.
201819: Convex optimization (12 hours). Master 1 degree at Toulouse School of Economics (TSE).
201718, 201819: Statistics (8 hours / year). Master 2 degree at ISAE/Supaero.
Engineers and students supervision (last update: July 2024)
PhD students, engineers and postdocs
2024.: L. Benamrouche (Research Engineer AMIES/IMT). Development of responsive websites to communicate on the mathematical innovations made at IMT.
2024.: V. Shilova (CIFRE PhD student at IMT / Artefact). Statistical analysis and image vision models for cosmetic data. Cosupervision with J.M. Loubes (Pr IMT) and E. Malherbe (Artefact, Paris).
2024.: C. Ferrassou (PhD student at IMT / DGATA). Machine learning models for predictive maintenance. Cosupervision with R. Bouclier (Pr INSA/ICA/IMT), P. Escande (CR CNRS/IMT) and M. Duval (DGATA).
2022.: Z. Kheil (PhD student at INSERMIUCT / IMT). NeuralNetworks for medical image registration. Cosupervision with S. Ken (IR INSERM).
202124: F. Jourdan (PhD student at ANITI  IRIT/IMT). Models of bias, fairness and robustness in langage and conversation. Cosupervision with N. Asher (DR IRIT) and J.M. Loubes (Pr IMT).
202223: T. Tshiongo Kaninku (Engineer at Akkodis group / IMT). Biases and explainability of neuralnetwork decisions. Applications in tabular data, images and NLP (github).
202123: R. Thoreau (CIFRE PhD student at ONERA Toulouse / Magellium / IMT). Machine learning models for the analysis of hyperspectral images (github). Cosupervision with V. Achard (ONERA), X. Briottet (ONERA) and B. Berthelot (Magellium). PhD overview in French (ma these en 180 secondes)
202023: L. De Lara (PhD student at Mathematics Institute of Toulouse  Univ. Toulouse  Funded by Ecole Polytechnique). Towards fair and explainable AI (github). Cosupervision with J.M. Loubes (Pr IMT) and N. Asher (DR IRIT).
202023: E. Viala (PhD student at ONERA Toulouse  ISAESuparo / IMT). Machine Learning models for the analysis of LIDAR images. Cosupervision with N. Rivière abd P.E. Dupouy (ONERA Toulouse)
2019: E.. Grossiord (Postdoc at IMT  1 year). Mutlimodal registration of 3D wholebody medical images. Cosupervision with F. Malgouyres (Pr IMT).
2018: V. K. Ghorpade (Postdoc at IMT  1 year). Registration of 3D wholebody medical images. Cosupervision with F. Malgouyres (Pr IMT).
20172019: T. Trang Bui (PhD student in applied mathematics at INSA Toulouse/Paul Sabatier University). Development of new strategies for the analysis of complex data. Cosupervision with J.M. Loubes (Pr IMT) and P. Balaresque (CR1 CNRS, AMIS  UMR5288).
Trainees
2024: C.T. Nguyen (4th year INSA in applied mathematics  3 months). XAI techniques for weather forecasting. Collaboration with CNRM/MétéoFrance.
2024: V. Blondel (highschool student  2 weeks). Reimplementation of PyTorch practicals in Pytrochlightning (notebooks).
2023: A. Perrin (5th year Paul Sabatier University in applied maths  5 months). Gradient boosting and fairness.
2023: V. Lafargue (4th year Paul Sabatier University in applied maths for data mining  4 months). Group Explainability in AI.
2023: T. Gouaichault (4th year Paul Sabatier University in applied maths for data mining  4 months). Defining fair VAEs.
2023: H. Lelievre (5th year INSA in applied mathematics 4 months). New DNN architechtures for sport data.
2023: S. Azeau (2nd year prepa INPT  6 weeks)  Learning with few observations in hyperspectral imaging.
2022: R. Lapeyre (4th year Paul Sabatier University in applied maths for data mining  4 months). Statistical analysis of data out of speech samples for Parkinson's disease detection. Cosupervision with S. Déjean (IR UPS).
2022: M. Cabaret (2nd year prepa INPT  6 weeks). Detection and control of algorithmic bias in NLP.
2021: M. Baldé (4th year INSA in applied mathematics 4 months). Technical and legal aspects of image registration using ML models. Cosupervision with S. Ken (IR INSERM/IRIT, Cancer Institute of Toulouse). Informal collaboration with M. Prudet and R. Pons (UT1  Law dept. of U. Toulouse) through ANITI.
2021: A. Scardigli (4th year EPFL / ENS  2 months). Segmentation and classification of remote sensing (sentinel) images using neural network models. Cosupervision with R. Jatiault (Univ. Perpignan, CEFREM / OMP).
2021: R. Moine (4th year INSA in computer science  2 months). Classification of remote sensing (sentinel) images using neural network models. Cosupervision with R. Jatiault (Univ. Perpignan, CEFREM / OMP).
2021: J. Cotxet (2nd year prepa INPT  6 weeks). Neural network models for ECG diagnosis.
202021: A. Faure (5th year Paul Sabatier University in computer science  6 months). Segmentation of 3D medical images using a semiautomatic method and neural networks. Cosupervision with S. Ken (IR INSERM/IRIT, Cancer Institute of Toulouse).
202021.: N. EnjalbertCourrech (M2 SID student in Data Science  Univ. Toulouse  1 year workstudy contract). Valorization of two statistical learning algorithms. Cosupervision with P. Neuvial (CR IMT).
2020: S. Benchelabi, N. Huynh, M. Noble, G. Sagot (hands on project of Ecole Polytechnique  20 hours of meetings with the students). Explainability of neural network predictions on the CeleA dataset and ECG signals. Cosupervision with J.M. Loubes (Pr IMT/ANITI).
2020: E. Viala (5th year Paul Sabatier University in applied maths  5 months). Neural Network models for the prediction of pollution impact in atmospheric chemistry. Cosupervision with F. Solmon (CNRS  Laboratoire d'aérologie/OMP)
2020: L. De Lara (5th year Ecole Polytechnique  6 months). Towards a fair and explainable AI using conterfactual models. Cosupervision with J.M. Loubes (Pr IMT) and N. Asher (DR IRIT)
2020 : I. Zniber (5th year Paul Sabatier University in applied maths  2 months). Interpretability of neural network decision rules based on multichannel signals .
2020 : J. De La Serna (5th year Paul Sabatier University in applied maths  2 months). Machine learning models for the analysis of NLP data out of Twitter (github).
2019: E. Letournel and A. Ecoffet (hands on project of Ecole Polytechnique  10 hours of meetings with the students). Studying the algorithmic bias in predictive algorithms to detect potential discriminations. Cosupervision with J.M. Loubes (Pr IMT/ANITI).
2019: B. Allorant (3rd year ENS Lyon  2 months). GPGPU solutions to speedup the estimation of a nonlinear 3D PDE. Application to image registration.
2019: A. Gossard (5th year INSA in applied mathematics  5 months). Denoising and regularization models for the analysis of LIDAR images. Cosupervision with P.E. Dupouy and N. Rivière (ONERA Toulouse)
2019: K. Niang (4th year Paul Sabatier University in applied maths for data mining  3 months). Definition of a deep learning strategy to predict polution levels based on multimodal satellite information. Cosupervision with F. Solmon (CNRS  Laboratoire d'aérologie/OMP)
2018: R. Vaysse (4th year Paul Sabatier University in applied maths for data mining  5 months). Statistical analysis of data out of speech samples for Parkinson's disease detection. Cosupervision with S. Déjean (IR UPS) and J. Farinas (Mcf UPS  UMR5505)
2018: L. Barros (3rd year IUT Computer Science Toulouse  3 months). Embedding Python, C++ and R apps with Docker.
2018: L. Riou (2nd year prepa INPT  6 weeks). GPU optimization of a stochastic algorithm by interfacing R and OpenCL. Cosupervision with Dr. P Neuvial (CR CNRS IMT)
20172021: C. Champion (PhD student in applied mathematics at INSERM/Mathematics Institute of Toulouse). Development of new strategies for the analysis of complex data. Cosupervision with J.M. Loubes (Pr IMT) and R. Burcelin (DR INSERM).
2017: V. Brès (5th year student at ENSEEIHT in Applied Maths/Computer Science  6 months). GPU computing with OpenCL to speedup large graph clustering algorithms.
2017: S. Lebreton (5th year student at ENSEEIHT in Applied Maths/Computer Science  6 months). Development of a C++ plugin in 3DSlicer for the semiinteractive segmentation of 3D medical images. Cosupervision with F. Malgouyres (Pr IMT).
2017: N. Artigouha (4th year INSA Toulouse in Computer Science  2 months). Using the C++ Boost Graph Library to for the analysis of large graphs + Graph visualisation at predefined coordinates in Cytoscape (plugin here).
Ongoing training, technical seminars, and scientific popularization (last update: July 2024)
June 2024: Detecting and tackling algorithmic biases in AI. Talk given for the SICD network.
March 2024: XAI and algorithmic bias in AI. Talk given for the PEPR M4DI.
March 2024: Towards trustable and legally compliant IA. Two talks organized by Club CNRS Entreprises, the first one being for CTOs and CEOs, and the second one to polititians and civil society representatives. In collaboration with J.M. Loubes (IMT/UT3/ANITI). Picture 1 and picture 2.
202324: Three talks/courses on neural networks, given in the fidle online sessions 2024 (in French  ANF CNRS).
Orator in the sequence 2 (From data to models), sequence 4 (AI, Society, Laws and Ethics), and sequence 5 (Maths and DNNs). All talks are available on youtube.
202324: Three times two days professional training sessions in XAI and robust ML. Sessions organized by CNRS formation entreprises and in the compagnies. In collaboration with J.M. Loubes (IMT/UT3/ANITI).
June 2023: XAI and algorithmic bias in AI. 2 days course for professionals with CNRS formation entreprises. In collaboration with J.M. Loubes (IMT/UT3/ANITI). All information is given here and there
May 2023: Introduction to Machine Learning and Deep Neural Networks. Two hours talk/course given at IRAP/OMP (Astrophysics and Planetology Research Institute of Toulouse).
May 2023: Law and A.I.. One hour talk given in collaboration with Ronan Pons during the EFELIA spring school on AI and social sciences (link).
March 2023: Towards trustable A.I.. Talk given in collaboration with JeanMichel Loubes as a CNRS Formation Entreprise webminar.
Talk available on youtube.
February 2023: Law and A.I.. Talk given in collaboration with Ronan Pons as a Citoy'Liens seminar.
Talk available on youtube: part 1 and part 2.
January to April 2023: Three talks/courses of 2 hours each on neural networks, given in the fidle online sessions 2023 (in French  ANF CNRS).
Orator of sequence 4 (mathematical aspects of neural networks), sequence 07 (PyTorch), and sequence 14 (AI and law/ethics). All talks are available on youtube.
November 2022: Algorithmic Bias and explainability in Machine Learning. 40 minutes talk at Phimeca seminar (link).
October 2022: Research in machine learning and data/software licences. 20 minutes talk at DataNoos symposium (link).
September 2022: Algorithmic Bias in Artificial Intelligence. 1h talk at CBI symposium (link).
June 2022: Algorithmic Bias in Artificial Intelligence. 40 minutes talk at LVMH HR seminar.
May 2022: Introduction to Deep learning. 1 hour tutorial at JRES 2022 with J.L. Parouty (CNRS Grenoble) and S. Arias (INRIA Grenoble).
January to April 2022: Three talks/courses of 2 hours each on neural networks, given in the fidle online sessions 2022 (in French).
Orator of sequence 4 (mathematical aspects of neural networks), sequence 11 (PyTorch), and sequence 14 (AI and law/ethics).
November 2021: Three explainability techniques in Machine Learning. 1 hour talk at the CIMIAOC seminar (slides).
October 2021: Algorithmic bias of machine learning models in natural langage processing. 40 minutes talk at a meeting of the GDR TAL. Talk in collaboration with F. Jourdan (IRIT/IMT/ANITI), J.M. Loubes (IMT/ANITI) and N. Asher (IRIT/ANITI).
September 2021: Explainable AI and the future legal regulation of AI in the European Union. 50 minutes talk at the
ECF national congress.
Talk given in collaboration with R. Pons (Law dept of Univ. Toulouse/ANITI) to accountants (picture).
April 2021: Two talks/courses of 2 hours each, given in the fidle online sessions 2021 (given in French).

Course 1:
Demystifying the mathematical aspects of neural networks
(Sequence 8  slides,
video)

Course 2: Introduction to PyTorch
(Sequence 9  slides,
video)
March 2021: Explainability techniques for BlackBox decision rules in ML. Invited talk at Marseille Astrophysics Laboratory (LAM).
January 2021: Introduction to Pytorch. Courses+practicals of three hours at JDEV2020. Fully given online.
November 2020: Introduction to Machine Learning. Two courses+practicals of three hours each with Alexandre Boucaud (CNRS Research Engineer, APS, Paris) at JDEV2020. The first course mainly focuses on the data and the second one on the models. Fully given online.
March 2020: Explainability and Fairness of Blackbox decision rules in AI. One hour talk with Ronan Pons (PhD student ANITI) at the Meetup Machine Learning Pau (slides in French).
March 2020: Introduction to Machine Learning. One day ongoing training event for high school lecturers in Toulouse area (course + practicals in Python). Event managed by Yohann Genzmer (lecturer IMT).
March 2020: Explainability and Fairness of Blackbox decision rules in AI. 40 minutes talk at the seminar statistics  engineering, gathering applied statisticians from different labs in Toulouse.
October 2019: Explainability and Fairness of Blackbox decision rules in A.I. One hour talk at the DEVLOG ongoing training event APSEM2019 (Slides in French:
Part 1, Part 2, Part 3).
August 2019: Introduction to Machine Learning. Two hours course and practical at CIMI RechercheMIDI Summercamp.
July 2019: 20 hours course and practicals at Master School on Data Science and Geometry.
 Week 2: Ten hours course and practicals in Machine Learning (slides of day 1:
Intro to M.L. / Intro to Python for M.L.).
 Week 4: Ten hours practicals in (1) Optimal transport for histogram equalization in images, (2) Diffeomorphic image registration with LDDMM, and (3) Neural networks with PyTorch.
November 2018: Introduction to Machine Learning. Two hours talk at the CNRSINRA ongoing training event (action de formation) APSEM2018 (slides in French).
November 2018: Using TPUs in Deep Learning. One hour seminar at the National Institute for Space and Aeronautics (ISAESupaero).
March 18: Introduction to Machine Learning (18 hours). VNUHCM  University of Science, HoChiMinh city, Vietnam.
January 2018: Data analysis and Machine learning with Python using the Scikitlearn module  Intermediate level. One day with courses and practicals at Observatoire MidiPyrénées.
January 2018: Introduction to GPU computing and OpenCL. Three hours seminar for Master students at the National Institute for Space and Aeronautics (ISAESupaero).
July 2017: JDEV 2017 (Development days  CNRS/INRIA/IRSTEA/INRA/IRD ongoing training event), Marseille. Lecture notes (in French):
 T7 AP01 (Initiation à Python),
 T7 A01 (Python Apprentissage),
 T7 GT02 (Retour d'experience sur OpenCL),
 T7 GT03 (Python Pandas vs R),
 T7 plénière (Apprentissage statistique pour la recherche par les données).
April 2017: Python for Scientists  Intermediate level. Three days of courses and practicals at CNRS  Observatoire MidiPyrénées.
Lecture notes (in French):
January 2017: A short introduction to GPU computing and OpenCL. Seminar statistics  engineering, gathering applied statisticians from different labs in Toulouse (slides in French).
Former activity (≤ 2016)
Teaching
201617: Lectures and practical courses in Image Analysis (8 hours). Master 2 in Mathematical Engineering. Université Paul Sabatier.
201617: Practical courses in Statistics (16 hours). Master 2 degree at ISAE/Supaero.
200506: Tutorials in Stochastic Process applied to heterogeneous media (16 hours).
Master 2 degree in Mechanics. Université Paul Sabatier.
200506: Lectures and practical courses in Computational Fluid Mechanics (32 hours). Lecture notes here.
The resolution methods for non linear PDEs in 1D and 2D are developed in this lecture.
Masters 1 degree in Mechanics. Université Paul Sabatier.
200506: Projects in Numerical simulation. Masters degree in mechanics. Université Paul Sabatier.
200506: Practical courses in Fluid Mechanics (18 hours). Preliminary degree in Mathematics and Computer Science applied to Sciences. Université Paul Sabatier.
200405: Lectures and practical courses in Computational Fluid Mechanics (44 hours). Master 1 degree in Mechanics. Université Paul Sabatier.
200405: Project in Numerical simulation (Doubly driven cavity).
Masters degree in mechanics. Université Paul Sabatier.
200405: Tutorials in Point Mechanics (20 hours). Preliminary degree in Mathematics and Computer Science applied to Sciences. Université Paul Sabatier.
200304: Lectures and practical courses in Computational Fluid Mechanics (44 hours). Master 1 degree in Mechanics. Université Paul Sabatier.
200304: Tutorials in Point mechanics (20 hours). Preliminary degree in Mathematics and Computer Science applied to Sciences. Université Paul Sabatier.
Students supervision (last update: September 2020)
2016: D. Grasselly (4th year INSA Toulouse  3 months  Now student at INSA Toulouse). Development of a Matlab code for the registration of lung images with sliding conditions. Cosupervision with J. Fehrenbach Loubes (Mcf IMT/UPS).
2016: M. Verdier (4th year INSA Toulouse  3 months  Now student at INSA Toulouse). Development of a Python code for the induction of Bayesian networks from medical data .
2016: A. Martin (3rd year ENS Lyon  3 months  Now student at ENS Lyon). Development geodesic shooting algorithm for the registration of 3D medical images using OpenCL
.
2016: M. Ralle (5th year Univ. Paris Orsay  4 months  Now aggregate high school lecturer). Statistical analysis of the cochlear coil. Cosupervision with J.M. Loubes (PR IMT/UPS).
2016: S. Roudiere (2nd year prepa INPT  6 weeks  Now student at Phelma PET Grenoble). Devlopment of nonrigid registration algorithm in Python applied to the motion tracking of US heart images. Cosupervision with MarieLaure Boizeau (IE INSERM US006 CREFRE, CNRS)
2015: T. Berriat (4th year INSA Toulouse  3 months Now student at INSA Toulouse). Statistical analysis of the cochlear coil. Cosupervision with J.M. Loubes (PR IMT/UPS) and J. Dumoncel (IE AMIS/CNRS).
2015: A. David (2nd year prepa INPT  6 weeks  Now student at ENSEEIHT). Devlopment of a GUI in Python to register 3D images.
2015: N. Artigouha (3rd year IUT Computer Science Toulouse  3 months  Now student at INSA Toulouse). Plugin development in Java to interface R and Cytoscape.
2014: A. Choury (former INSA student  6 months  Now founder and president of Alykis/consulting). Statistical analysis of seismic wave propagation measures. Cosupervision with J.M. Loubes (PR IMT/UPS) and P. Besse (PR IMT/INSA).
2014: S. Tobji (2nd year IUT informatique Toulouse  3 months  Now student at Univ. Paul Sabatier/IRIT). Development of a new anisotropic smoothing strategy in ITK. Cosupervision with J. Fehrenbach (McF IMT/UPS).
2013: L. Dolius (M2R Radiophysics and medical imaging Toulouse  6 months  Now engineer with Cap Gemini Toulouse). Quantitative Analysis of 3D brain images. Cosupervision with C. Fonta (DR CerCo/CNRS) and M. Mescam (McF CerCo/UPS).
2010: A. Camphuis (M1 Supelec  2 months  Now Petroleum Engineer with Perenco). Validation of an image registration algorithm. Cosupervision with F.X. Vialard (Former postdoc Imperial College London / now McF Paris Dauphine).
2009: D.P. Zhang (PhD student  unformal supervision  Now with Exascale Computing/AMD Research Group). Motion correction of coronary vessels in CT images. Main supervisor: D. Rueckert (Pr Imperial College London).
2008: A.L. Fouque (M2R ENS Cachan  5 months). Analysis of the BOLD signal in fMRI. Main supervisor: P. Ciuciu (CR CEA Saclay).
2005: V. Gratsac (M2 Computer Science Univ. Nantes  6 months  Now engineer with SYNEIKA, Rennes). Tensor voting for large vascular network images. Main supervisor: F. Plouraboue (DR IMFT/CNRS).
2004: G. Gujadur (L3 Mecanics  3 months  Now at Siemens Industrial Turbomachinery AB, Sweden). Quantitative analysis of the vascular network in synchrotron images. Main supervisor: F. Plouraboue (DR IMFT/CNRS).
Ongoing training, technical seminars, and scientific popularization
November 2016: Three applications of data mining. Seminar for Master students at
Ecole Nationale Supérieure d'Electrotechnique, d'Electronique, d'Informatique, d'Hydraulique et des Télécommunications (INP ENSEEIHT), Toulouse.
March 2015: Mathematical Engineering, what is that? Seminar to explain to high school students what can be an engineer in applied mathematics  Formation métier Activités (slides in French. See also the slides of S. Déjean or a document edited by ONISEP).
October 2014: Introduction to Cytoscape for large graphs visualization and analysis. Seminar statistics  engineering, gathering applied statisticians from different labs in Toulouse (slides in French).
July 2014: Introduction to R. Personnel from Université Paul Sabatier. (for more information, see Sébastien Déjean's webpage ).
June 2014: Introduction to statistics. Personnel from Université Paul Sabatier. (for more information, see Sébastien Déjean's webpage ).
January 2014: Introduction to Python for scientists (slides in French). Institut de Mathématiques de Toulouse.
July 2013: Introduction to R. Personnel from Université Paul Sabatier.
June 2013: Introduction to statistics. Personnel from Université Paul Sabatier.
December 2013: Three applications of data mining. Seminar for Master students at
Ecole Nationale Supérieure d'Electrotechnique, d'Electronique, d'Informatique, d'Hydraulique et des Télécommunications (INP ENSEEIHT), Toulouse.
March 2013: Using SVN for collaborative projects. Seminar statistics  engineering, gathering applied statisticians from different labs in Toulouse.

