Karl-Augustt Alahassa is at the end of his doctoral studies at the University of Montreal in Bayesian statistics applied to deep learning. He develops models of neural networks adaptable to small subsets of data and taking into account new structures of weight and bias. He is very familiar with machine learning algorithms (any application combined, including images and sounds). Karl-Augustt graduated in 2016 as a data engineer from ENSAE (Dakar, Senegal). Since then, he has several work experiences in the computer and artificial intelligence fields. Karl-Augustt is passionate about research and technology development with several certifications to his credit, including that of MIT (Big Data & Social Physics, 2014), J-PAL (Evaluating Social Programs, 2014), Stanford University (Game theory, 2014).