PostDoc, Guinea Infectiology Research and Training Center, Guinea
Dr. Hounmenou Gbêmêmali Castro is a distinguished post-doctoral researcher in Biostatistics and Artificial Intelligence at the Centre de Recherche et de Formation en Infectiologie de Guinée (CERFIG), Université Gamal Nasser de Conakry, Guinea. Previously, he was a lecturer-researcher at the Laboratoire de Biomathématiques et d’Estimations Forestières of the University of Abomey-Calavi, Benin. He actively participates in various scientific communities, contributing to fields like biostatistics, machine learning, and artificial intelligence.
Profile:
Dr. HOUNMENOU Gbêmêmali Castro, Ph.D. 🔗
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Education 🎓
Dr. Hounmenou obtained his Ph.D. in Statistics-Probability, specializing in Machine Learning and Data Mining, from the Africa Center of Excellence in Mathematics Sciences, Computer Science, and Applications at the Institute of Mathematics and Physical Sciences (IMSP) in Benin, in 2021. He also holds a Master’s degree in Biostatistics and a Diploma of Conception Engineer in Environmental Engineering Techniques, among other academic credentials from the University of Abomey-Calavi, Benin.
Experience 💼
Dr. Hounmenou’s academic career includes lecturing on topics ranging from forestry inventories to big data and cloud computing. His research spans epidemiology, public health, biostatistics, and AI applications. He has co-supervised over twenty theses and is actively involved in several research groups and scientific communities across West Africa and beyond.
Research Interests 🔬
Dr. Hounmenou’s research focuses on biostatistics, artificial intelligence, machine learning, epidemiology, and public health. He has a keen interest in the application of AI and ML techniques in agriculture, environmental science, and health data analysis.
Awards 🏆
Dr. Hounmenou has received several accolades, including the Best Poster award for his doctoral thesis works at the World Bank’s “Doctoriale des Centres d’Excellence du Bénin” in 2021. His contributions to scientific research and education have been widely recognized.
Scientific Publications 📚
Tahi, S.P.G., Hounmenou, G.C., Houndji, V. R., & Kakaï, R. L. G. (2024). An experimental analysis of traditional machine learning algorithms for maize yield prediction. Contemporary Mathematics. In press.
Houetohossou, S. C. A., Houndji, V. R., Hounmenou, G.C., Sikirou, R., & Kakaï, R. L. G. (2023). Deep learning methods for biotic and abiotic stresses detection and classification in fruits and vegetables: State of the art and perspectives. Artificial Intelligence in Agriculture, 9, 46-60.
Bangoura, S.T., Hounmenou, G.C., Sidibé, S., Camara, S.C., Mbaye, A., Olive, M. M., Camara, A., Delamou, A., Keita, A.K., Delaporte, E., Khanafer, N., & Touré, A. (2023). Exploratory analysis of healthcare workers’ knowledge, attitudes, and perceptions of arboviruses in the context of surveillance in the Republic of Guinea. PLOS Neglected Tropical Diseases, 17(12), e0011814.
Deguenon, S. D. D. M., Hounmenou, G. C., Adade, R., Teka, O., Toko, I. I., Aheto, D. W., & Sinsin, B. (2023). Simulation of the Impacts of Sea-Level Rise on Coastal Ecosystems in Benin Using a Combined Approach of Machine Learning and the Sea Level Affecting Marshes Model. Sustainability, 15(22), 16001.
Hounmenou, G. C., Gneyou, E. K., & Glèlè Kakaï, R. (2021). A formalization of the general mathematical expression of multilayer perceptron neural networks. Preprints.