Affiliation: NYU Abu Dhabi
Education: BSc. in Computer Science from Carnegie Mellon University; MSc. in Language Technologies from Language Technologies Institute at Carnegie Mellon University
Research Areas: Computational Social Science; Data Science; Voice and Audio Forensics; Affective Computing; Machine Learning; Science of Science; Digital Health; Network Science
Shahan is a Research Associate in the Computational Social Science Lab at New York University Abu Dhabi (NYUAD), UAE. He received his MSc. in Language Technologies from Language Technologies Institute at Carnegie Mellon University (CMU) in Pittsburgh, and his BSc. in Computer Science from Carnegie Mellon University in Qatar. Shahan’s research focuses on problems that have a significant societal impact, where he studies people and their communication to understand and uncover the complex and inconspicuous societal behaviors, and devise solutions to address societal problems.
Prior to joining NYUAD, as part of his directed research for Master’s at CMU, Shahan has collaborated with the Software Engineering Institute (SEI) and the Department of Defense (DoD) on a project focused on the development of algorithms for speech emotion recognition using microarticulometry and deep learning. His master’s was funded by the Center of Machine Learning for Health (CMLH) at CMU. As a CMLH fellow, he has completed a master’s thesis on analyzing misinformed online health communities (i.e. anti-vaccination communities and COVID-19 communities) in terms of their network and sociolinguistic characteristics. He has also worked as a research scholar at Carnegie Mellon for two years, as part of which he has worked with the Department of Homeland Security (DHS), and the United States Coast Guard (USCG) on voice forensics projects to devise machine learning based algorithms for profiling criminals from their voice.
His work has been accepted and published in the Journal of Medical Internet Research (JMIR), Interspeech, CIKM, SBP-BRiMS, IJCNN, WebSci, and other international conferences. Cumulatively, his work contributes to the fields of computational social science, digital health, affective computing, machine learning, and voice forensics.