Hi, I am a PhD student in Machine Learning and Optimization. My research focuses on optimization methods (nonlinear convex and non-convex optimization in particular) of models applied to NLP, more precisely on languages with low-resource corpora.

I have been practicing data science for more than 2 years. I owe my experience to the data science and MLOPs projects that I had to lead. These projects aim to solve various and differing real-life problems (from images, to detect if a plant is sick; sentiment analysis; protein function prediction; landslide prevention thanks to satellite imagery; credit scoring; etc.), addressing thematics such as computer vision, bioinformatics, natural language processing and speech recognition .

I have know-how on how machine learning algorithms (linear regression, SVM, decision tree, boosting, HMM, etc.) and deep learning (embedding, CNN, seq2seq, LSTM, transformers and transformers-based models) work, how to configure them, modify them according to the problems faced.