Dr. Moussa Doumbia is an accomplished educator and researcher with a Ph.D. in Mathematics from Howard University (2017) and a Bachelor’s Degree in Mathematics from the University of the District of Columbia (2007). Dr. Doumbia has made significant contributions to data science and mathematics education. He developed, implemented, and taught an introductory data science course and played a crucial role in establishing a mathematics major with a concentration in data science at Howard University.

As a key faculty member of the Virtual Applied Data Science Training Institute (VADSTI), Dr. Doumbia has been instrumental in advancing data science education. He participated in the Microsoft Visiting Data Science Professor Program in the summer of 2021 and co-created Howard University’s Coursera course titled “Linear Algebra for Data Science Using Python.” His collaborative efforts extend to the Social Network Research Program, a joint initiative between Howard University, the University of Delaware, and the NSA (2020-2021). 

Currently, Dr. Doumbia is serving as a Visiting Professor at the United States Military Academy West Point (June 2024 - June 2025). He also holds the position of Master Instructor in the Department of Mathematics at Howard University, where he has been a faculty member since 2017. His research interests include differential privacy, the application of ordinary differential equations to model infectious diseases, optimal control theory for vector-borne diseases,
and quantum computation, mathematics, and physics. Dr. Doumbia has published a paper on malaria incidence and asymptomatic malaria infections and has supervised numerous student projects on topics such as COVID-19 data analysis and predictive modeling. His excellence in teaching and research has been recognized with several awards, including the Outstanding Faculty of the Mathematics Department (2021-2022) and the Best Oral Presenter award at Howard University’s Research Day (2013).

In addition to his academic achievements, Dr. Doumbia has been actively involved in various conferences, workshops, and invited talks, sharing his expertise on malaria infections, differential privacy, and mathematical modeling. He continues to inspire and mentor students and researchers, contributing to the growth and development of data science and mathematics. His research interests also include machine learning, natural language processing (NLP), and
differential privacy.