Ildephonse Nizeyimana

Ildephonse Nizeyimana is a Postdoctoral Fellow in Biostatistics at Moi University. He earned his PhD in Mathematics, specializing in Statistics, in June 2024 from the Pan African University, Institute for Basic Sciences, Technology, and Innovation (PAUSTI). He has a background in Applied Mathematics from the University of Rwanda, where he served as an Assistant Lecturer in Applied Mathematics and Statistics at the College of Business and Economics since 2018. His current research focuses on Personalized Medicine, specifically examining its potential application in the treatment of elderly individuals living with HIV in Kenya.

Aweke Mitku

Dr Aweke Mitku is a Postdoctoral Fellow in Biostatistics at the Global Change Institute, University of the Witwatersrand in Johannesburg, South Africa. He completed his PhD in Biostatistics from the University of KwaZulu-Natal in 2020 and worked as an assistant professor at Department of Statistics, College of Science, Bahir Dar University, Ethiopia. His postdoctoral research focuses on spatio-temporal statistical methods to study the relationship between climate change and PM2.5 air pollution on under-five mortality in Sub-Saharan Africa. His work represents an important intersection of advanced statistical methodology, climate change, and public health research in the African context.

Souand Peace Gloria TAHI

Souand Peace Gloria TAHI is a biostatistician, data analyst, and AI researcher. A recipient of DAAD, AI4D, and Mawazo scholarships, she conducted her PhD at the Laboratory of Biomathematics and Forest Estimation (LABEF), where she developed machine learning models to optimize maize yield prediction in Benin, integrating climate, soil, and remote sensing data.

Currently a postdoctoral researcher at LABEF, she uses structural equation modeling to explore the complex interactions between environmental, socioeconomic, and demographic factors influencing tropical disease patterns in Sub-Saharan Africa. Through her multidisciplinary research, she aims to bridge AI, agriculture, and public health for sustainable development in Africa.

Awol Seid Ebrie

Awol Seid Ebrie is a post-doctoral fellow in Biostatistics at the Wits School of Public Health (SPH), University of the Witwatersrand, Johannesburg, South Africa. He holds a Ph.D. in Industrial Data Science & Engineering from Pukyong National University and Pusan National University, South Korea (2021–2024); an M.Sc. in Biostatistics from Jimma University (2010–2012) and a B.Sc. in Statistics from the University of Gondar (2005–2008), Ethiopia. With extensive academic experience in Ethiopian higher education institutions, Awol has a strong background in biostatistical methodologies, machine & deep learning algorithms, and data science applications.

His post-doctoral research focuses on developing AI and machine learning algorithms for disease prevention and personalized treatment, employing cutting-edge methodologies such as deep neural network-based causal inference, explainable AI (XAI), convolutional neural networks (CNN), and other state-of-the-art techniques. Beyond research, he is dedicated to enriching the academic environment at Wits SPH by participating in the teaching-learning process, mentoring students, conducting short-term trainings, and organizing workshops and seminars on emerging advancements in biostatistical and data science methodologies.

Awol is also committed to fostering collaborations and engaging in interdisciplinary public health studies. For collaboration opportunities or any queries, you can contact him at awol.ebrie@wits.ac.za or via WhatsApp at +27 82 401 9256.