Quantifying the Impact of COVID-19 on Essential Health Services: A Comparison of Interrupted Time Series Analysis using Prophet and Poisson Regression Models
Girmaw is a Staff Research Scientist in AI Science team at IBM Research Africa, mainly working on detecting and characterizing systematic deviations in data and machine learning models. He is also leading a project on representation analysis in dermatology academic materials in collaboration with Stanford University. At IBM, Girmaw also collaborates with Bill & Melinda Gates Foundation and Harvard University on data-driven insight extraction for maternal, newborn and child health. Prior to joining IBM, Girmaw worked as a Postdoctoral Researcher at the University of Oxford, where he primarily developed deep learning techniques to assist disease diagnosis. Girmaw completed his PhD at Queen Mary University of London, under the Erasmus Mundus Double Doctorate Program in Interactive and Cognitive Environments. His PhD research focused on computer vision and machine learning algorithms for human activity recognition using wearable sensors. He has worked in various research groups across Europe, including the UPC-BarcelonaTech (Spain), KU Leuven (Belgium), and INESC-ID (Portugal). Girmaw is an Executive Committee Member for IEEE Kenya Section, and he has been serving as a reviewer, program committee member and workshop organizer in multiple top-tier AI venues, including AAAI, ICLR, ICML and NeurIPS .