I'm Gabriel Watkinson, a Master's student at ENSAE IP Paris and MVA - ENS Saclay, two of the best French Engineering Grande Ecole in Data Science and AI.
In this repository, you will find submodules that link towards some of my projects.
- Internship in the Computational Bio-Imaging and Bioinformatics team at the ENS Biology Institute under the supervision of Auguste Genovesio and Ethan Cohen.
Multimodal models to learn common representation between molecules and the HCS images they are associated with.
Repo to download a subset of the JUMP HCS dataset.
| Projects | Links |
|---|---|
| MVA Projects | |
| Shape Analysis of Elastic Curves in Euclidean Spaces |    |
| Generative Modelling project on the paper: Likelihood training of SB using FB SDEs theory | |
| NLP project on CANINE | |
| NLP project on French politics | |
| Convex Optimisation Homework |  |
| ENSAE Projects | |
| Estimating behavioral parameters in animal movement models using a state-augmented particle filter | |
| NLP project on adversarial attack detection in NLP | |
| Bayesian Statistics project on the paper An Automated Prior Robustness Analysis in Bayesian Model Comparison | |
| Do Wasserstein-GAN approximate Wasserstein distances ? | |
First Author:
- Gabriel, W., Ethan, C., Nicolas, B., Ihab, B., Guillaume, B., & Auguste, G. (2023). Weakly supervised cross-model learning in high-content screening. arXiv preprint arXiv:2311.04678.
Coauthor:
- Bourriez, N., Bendidi, I., Cohen, E., Watkinson, G., Sanchez, M., Bollot, G., & Genovesio, A. (2023). ChAda-ViT: Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images. arXiv preprint arXiv:2311.15264.
MVA (which stands for Mathématique - Vision - Apprentissage) is the best French Reserch Master 2 for Data Science and Statistic Modelisation.
The course I followed are :
- Geometric Data Analysis (J. FEYDY)
- Fundamentals of Reproducible Research and Free Software (M. COLOM BARCO)
- Deep Learning (V.LEPETIT, M. VAKALOPOULOU)
- Convex Optimization and Applications in Machine Learning (A. D'ASPREMONT)
- Bayesian Machine Learning (R. BARDENET, J. ARBEL)
- Algorithms for speech and natural language processing (E. DUPOUX, B. SAGOT)
- Modèles Génératifs pour l’Image (B. GALERNE, V. DE BORTOLI)
- Audio Signal Processing – Time-frequency Analysis (E. BACRY)
- Computational Optimal Transport (G. PEYRE)
- Kernel Methods for Machine Learning (J. MAIRAL, M. ARBEL)
ENSAE is a school specialized in Statistics, Data Science, Economy and Finance. I specialized in Data Science, choosing the Data Science, Statistics and Learning track.
Some of the courses I validated are :
Third Year :
- Advanced Machine Learning (V. PERCHET)
- Bayesian Statistics (A. SIMONI)
- Ethics and responsibility in data science (P. TUBARO)
- Computational statistics (C. ROBERT)
- Hidden Markov models and Sequential Monte-Carlo Methods (N. CHOPIN)
- Machine Learning for Natural Language Processing (P. COLOMBO)
- Online learning and aggregation (A. TSYBAKOV)
- Bootstrap and Resampling Methods (E. LAPENTA)
- Optimal Transport (M. CUTTURI)
First and Second Years :
- Measure and Probability Theory (V.E. BRUNEL)
- Advanced Statistics (M. LERALSE)
- Stochastic Processes (N. CHOPIN)
- Simulation and Monte Carlo Methods (N. CHOPIN)
- Linear Time Series
- Econometry
- Financial Mathematics
- Introduction to Game Theory