Oluwagbemi, Dr Olugbenga

Biography & Qualifications

Dr. Olugbenga Oluwagbemi conducted his PhD research in the Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, United States of America, as a J. Williams Fulbright Research Scholar and defended his PhD Computer Science thesis in Covenant University, Nigeria. He also obtained an advanced certificate from the Rochester Institute of Technology, United States of America, under the sponsorship of the J. William Fulbright Scholarship Foundation. He received a BSc (Hons) Computer Science degree from the University of Ilorin, and an MSc Computer Science degree from the University of Ibadan, Nigeria. Olugbenga held a 2-year NRF Innovation postdoctoral research fellowship at the Department of Mathematical Sciences, Stellenbosch University (SU), South Africa. He was also an AfOx Visiting Postdoctoral researcher at the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, United Kingdom. Afterwards, he held another 2-year DAAD-funded and Oppenheimer Memorial Trust-funded postdoctoral research associate fellowship position at Stellenbosch University, South Africa.

He is a UK senior lecturer (associate professor equivalent in the US). He is a senior lecturer on Wikipedia in Computer Science at Middlesex University, UK. Before this, Olugbenga was an Associate Professor of Computer Science and IT at Sol Plaatje University, South Africa, where he led a task team to develop the concept note and established the Centre for Applied Data Science at the university. Before this, he held Senior Lecturer positions for 6 years across three (3) previous universities. He has also held Lecturer positions for 4 years and Assistant Lecturer positions for 2 years. He has research experience at various universities in the United States of America, the United Kingdom, South Africa, and Nigeria. He has the UK Royal Society’s endorsement for a Global Talent immigration stream. He is also a C2 NRF-rated established researcher in South Africa.

Learning & Teaching Interests

Olugbenga has over 18 years of teaching experience in Computer Science and Information Technology courses in different university settings.

Module Leader CST4125 Blockchain Technology (Jan 2024-September 2024)
Module Leader CST1530 Computer Networks CST1530 (As of Jan 2025)
Module co-teaching CST 4080 Ethics Aspects of Data Science (Since September 2023)
Module co-teaching CST 4080 Security Aspects of Data Science (May 2023 – Sept. 2023)

Research Output & Interests

Olugbenga conducts research in Artificial Intelligence in Health; AI in Biomedical data science; Computational modelling in health; Modelling the impact of climate change effects on the proliferation of infectious and non-infectious diseases; Computational Health Informatics; Bioinformatics (Computational Genomics); Informatics in medicine; Computer-Aided Diagnosis; Information Technology in Health; developing health informatics software and mobile applications for health management; Climate change modelling; Applied Data Science; Applied Machine learning. He has published in many prestigious international journals and conferences. He has also won some prestigious individual and collaborative international research grants.

Research Interests

Applied Artificial Intelligence
Applied Health Informatics
Computational Modelling in Health
Bioinformatics (Computational genomics)
Applied Computing
Applied Machine Learning
Applied Biomedical Data Science

Student Supervisions

Supervising 1 Postdoctoral researcher (to start Jan/Feb 2025)

Currently co-supervising 1 PhD student

Successfully supervised 17 MSc candidates

Successfully supervised 72 BSc Honours students

Funded Research & Knowledge Exchange Projects

AREF Grant to sponsor Postdoctoral Research Fellow to Middlesex University London, UK

More Information

Personal websites:

https://olugbengaoluwagbemi.weebly.com/postgraduate-supervisions-and-courses-taught.html

Publication Repository:

https://repository.mdx.ac.uk/researcher/80q33/dr-olugbenga-oluwagbemi

Email: O.Oluwagbemi @ mdx.ac.uk
Office: T131 Town Hall Building, Hendon Campus

Personal Website

Publications Repository

Facial emotion recognition and classification using the Convolutional Neural Network-10 (CNN-10)

Dada, E., Oyewola, D., Joseph, S., Emebo, O. and Oluwagbemi, O. 2023. Facial emotion recognition and classification using the Convolutional Neural Network-10 (CNN-10). Applied Computational Intelligence and Soft Computing. 2023. https://doi.org/10.1155/2023/2457898

Ensemble machine learning for Monkeypox transmission time series forecasting

Dada, E.G., Oyewola, D.O., Joseph, S.B., Emebo, O. and Oluwagbemi, O. 2022. Ensemble machine learning for Monkeypox transmission time series forecasting. Applied Sciences. 12 (23). https://doi.org/10.3390/app122312128

Application of deep learning techniques and Bayesian optimization with tree parzen estimator in the classification of supply chain pricing datasets of health medications

Oyewola, D., Dada, E., Omotehinwa, T., Emebo, O. and Oluwagbemi, O. 2022. Application of deep learning techniques and Bayesian optimization with tree parzen estimator in the classification of supply chain pricing datasets of health medications. Applied Sciences. 12 (19). https://doi.org/10.3390/app121910166

Insights into the impacts of and responses to COVID-19 pandemic: The South African food retail supply chains perspective

Omoruyi, O., Dakora, E.A. and Oluwagbemi, O. 2022. Insights into the impacts of and responses to COVID-19 pandemic: The South African food retail supply chains perspective. Journal of Transport and Supply Chain. 16. https://doi.org/10.4102/jtscm.v16i0.739

Using deep 1D convolutional grated recurrent unit neural network to optimize quantum molecular properties and predict intramolecular coupling constants of molecules of potential health medications and other generic molecules

Oyewola, D.O., Dada, E.G., Emebo, O. and Oluwagbemi, O. 2022. Using deep 1D convolutional grated recurrent unit neural network to optimize quantum molecular properties and predict intramolecular coupling constants of molecules of potential health medications and other generic molecules. Applied Sciences. 12 (14). https://doi.org/10.3390/app12147228

Towards resolving challenges associated with climate change modelling in Africa

Oluwagbemi, O., Hamutoko, J.T., Fotso-Nguemo, T.C., Lokonon, B.O.K., Emebo, O. and Kirsten, K.L. 2022. Towards resolving challenges associated with climate change modelling in Africa. Applied Sciences. 12 (14). https://doi.org/10.3390/app12147107

Bioinformatics, computational informatics, and modeling approaches to the design of mRNA COVID-19 vaccine candidates

Oluwagbemi, O., Oladipo, E.K., Kolawole, O.M., Oloke, J.K., Adelusi, T.I., Irewolede, B.A., Dairo, E.O, Ayeni, A.E., Kolapo, K.T., Akindiya, O.E., Oluwasegun, J.A., Oluwadara, B.F. and Fatumo, S. 2022. Bioinformatics, computational informatics, and modeling approaches to the design of mRNA COVID-19 vaccine candidates. Computation. 10 (7). https://doi.org/10.3390/computation10070117

Molecular dynamic simulation reveals structure differences in APOL1 variants and implication on pathogenesis of chronic kidney disease

Mayanja, R., Kintu, C., Diabate, O., Soremekun, O., Oluwagbemi, O., Wele, M., Kalyesubula, R., Jjingo, D., Chikowore, T. and Fatumo, S. 2022. Molecular dynamic simulation reveals structure differences in APOL1 variants and implication on pathogenesis of chronic kidney disease. Genes. 13 (8). https://doi.org/10.3390/genes13081460

Computational construction of a glycoprotein multi-epitope subunit vaccine candidate for old and new South-African SARS-CoV-2 virus strains

Oluwagbemi, O., Oladipo, E., Dairo, E., Ayeni, A., Irewolede, B., Jimah, E., Oyewole, M., Olawale, B., Adegoke, H. and Ogunleye, A. 2022. Computational construction of a glycoprotein multi-epitope subunit vaccine candidate for old and new South-African SARS-CoV-2 virus strains. Informatics in Medicine Unlocked . 28. https://doi.org/10.1016/j.imu.2022.100845

Resolving challenges associated with climate chance modelling in Africa

Oluwagbemi, O. 2020. Resolving challenges associated with climate chance modelling in Africa. Virtual DAAD ClimapAfrica Conference 2020. Virtual 29 - 30 Oct 2020

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