Draft:Sokhar Samb

Sokhar Samb is a Senegalese Mathematician, Computer Scientist, AI researcher and Data Engineer. She is a lecturer at Dakar American University of Science and Technology (DAUST), Head if data scientist team. She is a young girl with a master degree in applied mathematics and computer science and a master degree in big data and computer security. Sokhar is a machine learning engineer with a focus on Natural Language Processing (NLP). She is the founder Women Promoting Science to Younger Generation(WPSYG) with the objective to talk to students in High schools, to help them discover what the world of mathematics has outside class. And believes that STEM needs to be promoted on the younger generation in order to give them the change to develop a careers in science.

Early life and education
Sokhar Samb was born in a village of Loul Sessene in the Fatick region of Senegal, She grew up with a passion for mathematics and science in general. Her love for math made Friday her favorite day at primary school because it was the day of the week when they had to solve four operations and a practical problem. After high school, she was awarded a scholarship to pursue her studies in Applied Mathematics and Social Sciences at UGB. Her dream of becoming a Statistician Engineer motivated her to obtain a Master’s degree in Statistics and Probability at the same university. In the course of her studies, Sokhar was actively involved in the Mathematics Club, as well as the association of students from the Fatick region and equally the association of students from the school of Science and Technology. After completing her Co-op internship at CEPEI, a think tank in Columbia, Sokhar dreams of becoming a Data Science expert. Her biggest goal is to promote girls in science. She also works with drones by mapping out cities and towns in Senegal such as Dakar and Semone by capturing high - resolution aerial imagery and light detections.

Select publications

 * Toward More Meaningful Resources for Lower-resourced Languages
 * Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets