Ashenafi Zebene Woldaregay is a PhD candidate at the University of Tromsø – The Arctic University of Norway (UiT), Faculty of Science and Technology, Department of Computer Science. He was Graduate assistant II (2008-2009), Assistant lecture (2010-2012) and Lecturer (2013-2014) at the Department of Electrical and Computer Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia. He was chairman of Computer Engineering Program (stream) (2013-2014), coordinating and supervising different academic activities. During the period he was employed at the university, he has taught various Computer and Electrical Engineering courses such as Introduction to Computer programming, Programming in C++, Data structure, Algorithm Analysis and Design, Objected Oriented Programming with Java, Data Communication and Computer Networks, Applied Electronics, Modern Control System, Electrical Machine, Power Electronics and Motor Derive, Instrumentation Engineering and so many others.
He has supervised different community based training program, coordinating and supervising a group of students working on-site community based projects along with many BSc projects. He holds a BSc degree in Electrical Engineering from Hawassa University, Hawassa, Ethiopia. MSc degree in Computer Engineering from Addis Ababa Institute of Technology, Addis Ababa, Ethiopia. MSc degree in Telemedicine and eHealth from the University of Tromsø – The Arctic University of Norway (UiT), Faculty of Science and Technology, Department of Computer Science. He has co-supervised 3 master students, and group of BSc students on a course project with Professor Gunnar Hartvigsen at the Department of Computer Science, University of Tromsø – The Arctic University of Norway (UiT). Mr. Ashenafi is currently co-supervising one Master student. He was assistant instructor for Telemedicine and eHealth master course. He had worked as research assistant in SINTEF on the SARINOR project.
His current research (PhD) focuses on an early detection of infection incidences using self-recorded data from people with diabetes. The project is an interdisciplinary research involving machine learning, diabetes, motivation, mHealth, and public health surveillance. He has experience in the use of machine learning algorithms in image processing and biometric verifications systems. Generally, his interests lie on machine learning applications in healthcare. Moreover, areas of health informatics including digital disease outbreak detection, feedback system for people with chronic disease, medical sensor system, and motivational mechanisms in mHealth.