Teaching

List of courses, projects and internships I have been involved in.

Courses

Courses

Year 2021-2022

Title Level Program TD TP TD.eq
INFO741 - Embedded Systems Master 1 Initial   24 16
EASI741 - Random Signals Master 1 Initial   12 8
INFO642 - Signal and Image Bachelor 3 Initial   9 6
PROJ641 - Project-Based Learning Bachelor 3 Initial 4 7 8.67
Total     4 52 38.67

Year 2022-2023

Title Level Program TD TP TD.eq
INFO101 - Algorithms Bachelor 1 Initial 1.5 14 10.83
INFO501 - Number Systems and Algorithms Bachelor 3 Initial 10.5   10.5
PROJ641 - Project-Based Learning Bachelor 3 Initial   0.5 0.33
PROJ841 - Project-Based Learning Master 1 Initial 6 4 8
INFO701 - Python Programming (IAE Annecy) Bachelor 3 Initial 10.5   10.5
INFO642 - Signal and Image Bachelor 3 Initial   9 6
INFO851 - Embedded Systems Master 1 Initial   20 13.33
Total     28.5 47.5 59.5

Year 2023-2024

Title Level Program TD TP TD.eq
INFO101 - Algorithms Bachelor 1 Initial 9 12 17
INFO501 - Number Systems and Algorithms Bachelor 3 Initial 9   9
PROJ841 - Project-Based Learning Master 1 Initial 1.5   1.5
PROJ943 - Project-Based Learning Master 2 Initial 7.5 4 9
INFO851 - Embedded Systems Master 1 Initial   20 13.33
INFO941 - Concurrent Programming Master 2 Initial   24 16
Total     27 60 65.83

Year 2024-2025

Title Level Program TD CM TD.eq
EASI844 - Discrete-Time Systems Master 1 Initial 13 3 17.5
INFO501 - Number Systems and Algorithms Bachelor 3 Initial 9   9
Total     22 3 26.5

Project

  • Problem/Project-based Learning (APP): Proposal and supervision of a student project (3rd-4th year of engineering education) in collaboration with CREA (Altitude Ecosystem Research Center). Autonomous System SCAR (Snow Cover & Animal Recognition):
    • Physical measurements acquisition (temperature, humidity & brightness)
    • Image acquisition + processing (object detection, snow/greenery ratio)
    • Information transmission via LoRaWAN protocol
    • Real-time visualization of a set of indicators

Internships

  • Master 1 internship (2024, 4 months): Validation and benchmarking of machine learning methods for the characterization of wet snow by SAR imaging: Application to the Grandes Rousses area.
    Summary: Implementation of a benchmarking of machine learning methods for snow characterization by SAR imaging from the [D7] dataset.
    • Formatting existing codes for command-line use.
    • Development of new data labeling methods.
    • Improvement of resource exploitation (multi-threading) and cluster usage option.
    • Creation of a website and documentation.
      Valorisation: The developed processing chains have been made available with the creation of a dedicated website.
      The project on GitHub
  • 4th-year engineering education internship (2022, 3 months): Proof of concept of snow segmentation in webcam images by deep learning.
    Summary: Implementation of snow segmentation solutions in the context of webcam images with a proof of concept on images provided by CREA.
    • Realization of annotation of time series on 3 zones: in high mountain clear (>2000m), in medium mountain clear (between 1000m and 2000m) and in medium mountain in forest.
    • Implementation of deep learning networks dedicated to the segmentation task (U-net), and comparison of the obtained solutions with commonly used segmentation algorithms (thresholding, edge detection, Otsu).
      The project on Github
  • Bachelor’s degree internship (Brazil collaboration, 2023, 3 months): Wavelet Change Detection of Urban Areas in Multi-temporal Satellite Images and Supervised Classification of Change Detection Maps.
    Summary: In collaboration with Brazil (Aluisio Pinheiro, University of Campinas, Pr), improvement of a wavelet-based change detection method for the analysis of time series of satellite images (WECS), and development of a supervised classification method for the obtained change maps.
    • Processing of two time series of Sentinel-1 satellite images on Mexico and Reunion.
    • Improvement of the WECS method, by taking into account two scales (global and local) for change detection.
    • Use of the maps obtained by WECS on Reunion for the supervised classification of detected changes.
      The project on Github.