Picture of Dr. Tayo Obafemi-Ajayi

Dr. Tayo Obafemi-Ajayi

Associate Professor, Mace/Turblex Professor of Engineering

Missouri State University Cooperative Engineering Program


Dr. Tayo Obafemi-Ajayi is the Mace/Turblex associate Professor of Electrical Engineering at Missouri State University in the Cooperative Engineering program (joint agreement with Missouri University of Science and Technology). She is also the faculty director of the Computational Learning Systems lab and the site coordinator for the Missouri Louis Stokes Alliance for Minority Participation (MoLSAMP) at the university. She serves as the chair of IEEE CIS Bioinformatics and Bioengineering Technical Committee (BBTC). She also serves as a Technical Representative on the IEEE Engineering in Medicine and Biology Society (EMBS) Administrative Committee. Her research centers on development and applications of explainable machine learning algorithms. Motivated by practical needs in biomedical applications, her overall goal is to design intelligent systems that analyze large data sets to yield novel discoveries and make meaningful predictions.

Self-Maintained Website: 

Selected Publications     www.missouristate.edu/egr/cls

  • Orlenko A, Freda PJ, Ghosh A, Choi H, Matsumoto N, Bright TJ, Walker CT, Obafemi-Ajayi T, Moore JH. Cluster Analysis reveals Socioeconomic Disparities among Elective Spine Surgery Patients. in Pacific Symposium on Biocomputing. Jan 2024.
  • Ekuma G, Hier DB, Obafemi-Ajayi T. An Explainable Deep Learning Model for Prediction of Severity of Alzheimer’s Disease. in Proc. IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) 2023.
  • Matta J, Dobrino D, Yeboah D, Howard S, EL-Manzalawy Y, Obafemi-Ajayi T. Connecting Phenotype to Genotype: PheWAS-inspired Analysis of Autism Spectrum Disorder. Frontiers in Human Neuroscience-Brain Health and Clinical Neuroscience. 2022.
  • Yeboah D, L. Steinmeister, Hier DB, B. Hadi, Wunsch II DC, Olbricht GR, Obafemi-Ajayi T. An Explainable and Statistically Validated Ensemble Clustering Model applied to the Identification of Traumatic Brain Injury Subgroup. IEEE Access 2020 (8). p.180690-180705.
  • Al-Jabery K, Obafemi-Ajayi T, Olbricht GR, Wunsch II DC. Computational Learning Approaches to Data Analytics in Biomedical Applications. Academic Press, Elsevier, 2019.

Selected Honors and Awards

  • Missouri State University Bear Bridge Outstanding Mentoring for Faculty Award 2023.
  • Best Student Paper Award, IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Oct 2021.
  • College of Natural and Applied Sciences Faculty Research Award, Spring 2019.


Research Interests:

Explainable Artificial Intelligence, Control Systems, Bioinformatics, Data Mining




  • PhD in Computer Science, Illinois Institute of Technology (IIT), Chicago IL
  • MS in Electrical Engineering
  • BS in Electrical Engineering (High Honors)