Donald Okoth Ojwang is a mathematician and researcher specializing in applied mathematics, mathematical biology, machine learning, and infectious disease modelling. He holds a Bachelor of Science in Mathematics with First Class Honours from South Eastern Kenya University, Kenya, and a Master’s degree in Mathematical Sciences from the African Institute for Mathematical Sciences (AIMS), Senegal, where he graduated with a Very Good Pass.

His research focuses on applying mathematical and computational methods to understand complex biological systems, model disease transmission, evaluate intervention strategies, and support evidence-based public health decision-making. His research experience includes mathematical modelling of measles and COVID-19, numerical and analytical solutions of damped wave equations, seasonal precipitation analysis, and stage-structured modelling of fall armyworm infestation in maize farming. He is also involved in research on solving partial integro-differential equations using artificial neural networks.

Donald has experience in machine learning data preprocessing, research assistance, data quality management, and mathematics and statistics tutoring. He has presented his research at the Deep Learning IndabaX Senegal workshop and participated in training programmes focused on data analytics, cleaner air, malaria modelling, and public health analytics. His academic achievements include first prize in a three-minute thesis competition, a Mastercard Foundation Scholarship for his master’s studies, and recognition as the best graduating student in the School of Science and Computing during his undergraduate studies.

As an Organizing Committee Member, Donald brings an interdisciplinary perspective spanning mathematical sciences, computational research, and public health applications. His academic and research experience supports scientific programme development, interdisciplinary collaboration, and the advancement of innovative mathematical and computational approaches to scientific challenges.

Applied mathematics, mathematical modelling, machine learning, computational biology, infectious disease modelling, artificial neural networks, and data-driven approaches to biological systems. His interests also include computational methods for understanding disease dynamics, modelling biological processes, and exploring the application of AI and mathematical techniques in biomedical research, vaccine development, and public health decision-making.