Yasmine Hamdar is a Computational Social Scientist at Data-Pop Alliance. She has a BS and a MSc in Computer Science (emphasis on Machine Learning and Data Science). Throughout her career, she has worked as a Machine Learning Engineer with Google AI Impact challenge x AUB on the “Smart Irrigation” project for water preservation in the MENA region with the help of predictive modeling. She employed machine learning (ML) models in order to process multispectral and thermal satellite imagery, local weather data, and farmer-supplied agricultural data for estimating crop water use in near-real-time at the agricultural field scale.
She was also assigned as a Computational Linguistics and Natural Language Processing Expert at UNDP (United Nations Development Programme). She is experienced in data filtration, data tokenization, stemming and lemmatization, and word embeddings. She is also experienced in the field of computational linguistics with all 6 core areas: syntax, morphology, phonetics, phonology, semantics, and pragmatics. She implemented several classified projects for information extraction, named entity recognition (NER), sentiment analysis through natural language processing, and word sense disambiguation. Recently, she has been involved in the world of digital governance where she was a Digital Transformation Officer at UNDP in which she provided strategic support on using and integrating digital solutions.
Yasmine has also worked as an instructor at the American University of Beirut where she taught Artificial Intelligence, Software Engineering, Database Systems, and Computer and Programming for the Sciences.