Dr Temitayo Fagbola

Dr Temitayo Matthew Fagbola

Teaching Fellow

Faculty and Department

  • Faculty of Science and Engineering
  • Data Science AI and Modelling Centre (DAIM)

Qualifications

  • BEng
  • MSc
  • PhD / DPhil
  • PGCert (University of Hull)

Summary

I am a teaching fellow at the Centre of Excellence for Data Science, AI and Modelling at the University of Hull. My background is in Computer Science. I bagged BTech (2008), MSc (2011) and PhD (2015) degrees in Computer Science. My pedagogy research interest is in exploring innovative and sustainable use of AI-powered virtual and augmented reality simulations and experiences to enhance student engagement and knowledge retention, allowing for location-independent learning.

Understanding Artificial Intelligence

Mathematics of Artificial Intelligence

Deep Learning

Applied AI

Natural Language Processing

Recent outputs

View more outputs

Data

UCTH Breast Cancer Dataset

Eteng, I., Bisong, E., Fagbola, T., Ibrahim, M., Udosen, J., & Akpotuzor, S. (2023). UCTH Breast Cancer Dataset. [Data]. https://doi.org/10.17632/63fpbc9cm4.2

Journal Article

Special issue "Towards a higher education of the future: Transformational roles of edge intelligence"

Doshi, R., Hu, Y. C., Garg, L., & Fagbola, T. (2024). Special issue “Towards a higher education of the future: Transformational roles of edge intelligence”. Array, 22, Article 100332. https://doi.org/10.1016/j.array.2023.100332

An SAP Enterprise Resource Planning Implementation Using a Case study of Hospital Management System for Inclusion of Digital Transformation

Aroba, O. J., Owoputi, A. O., & Fagbola, T. M. (2023). An SAP Enterprise Resource Planning Implementation Using a Case study of Hospital Management System for Inclusion of Digital Transformation. International Journal of Computer Information Systems and Industrial Management Applications, 15(2023), 154-165

Presentation / Conference Contribution

DeepCAI-V3: Improved Brain Tumor Classification from Noisy Brain MR Images using Convolutional Autoencoder and Inception-V3 Architecture

Babaferi, E. V., Fagbola, T. M., & Thakur, C. S. (2024, August). DeepCAI-V3: Improved Brain Tumor Classification from Noisy Brain MR Images using Convolutional Autoencoder and Inception-V3 Architecture. Presented at 7th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, Mauritius

Deep Learning-Based Colorectal Cancer Image Segmentation and Classification: A Concise Bibliometric Analysis

Fagbola, T. M., Aderemi, E. T., & Thakur, C. S. (2024, August). Deep Learning-Based Colorectal Cancer Image Segmentation and Classification: A Concise Bibliometric Analysis. Presented at 7th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Mauritius

Research interests

* Social computing AI and NLP

* efficiency of methods and models, and data efficiency

* transfer and small-data learning

* generative deep learning, unsupervised deep learning and stochastic optimisation

* biomedical and public health applications

* fairness and ethical application of ML

Postgraduate supervision

Explainable and Precise Medical Report Generation from Multimodal Clinical Records

Privacy-Preserving Precise Medical Visual Question Answering

Privacy-preserving Synthetic Medical Image Generation for Computer-aided diagnosis

Awards and prizes

Inspired in Hull Awards

2023

The ‘Excellence through Feedback’ award - WINNER The ‘Excellence in Teaching’ - RUNNER-UP

Membership/Fellowship of professional body

FHEA

2024

Fellowship of the Higher Education Academy

National/International learned society/body role

MBCS

2024

Member, British Computer Society. The Chartered Institute for IT

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