Armstrong Aboah (Member, IEEE) was born in Kumasi, Ghana. He received the B.S. degree in civil engineering from the Kwame Nkrumah University of Science and Technology, in 2017, the M.S. degree in transportation engineering from Tennessee Technological University, in 2019, and the Ph.D. degree in transportation engineering, with a focus in computer vision and machine learning from the University of Missouri, Columbia, MO, USA, in 2022. His papers have been published at top venues, including CVPR, NeuIPS, and the Journal of Transportation Engineering. His papers have received more than 243 citations. He has secured competitive research funding from various agencies, including projects on extracting insights from naturalistic driving data and factors influencing transportation network company usage. His research interests include computer vision, transportation sensing, big data analytics, and deep learning, with a focus on intelligent transportation systems. He has authored over 15 peer-reviewed publications in these areas. He is a leading researcher on vision-based traffic anomaly detection and has published one of the first papers applying deep learning and decision trees to this problem. His paper on real-time multi-class helmet violation detection using few-shot learning techniques has also been impactful. Other novel research contributions include smartphone-based pavement roughness estimation using deep learning, prediction of bus delays across multiple routes, and automated retail checkout using computer vision.