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 Artificial Intelligence

Natural Language Processing

Recent outputs

View more outputs

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

Presentation / Conference Contribution

A Responsible AI Perspective to implementing Generative AI in Personalized Healthcare: Implications, Challenges and Future Directions

Fagbola, T. M., Dhiman, A., Mboli, J., & Mishra, B. (2024, October). A Responsible AI Perspective to implementing Generative AI in Personalized Healthcare: Implications, Challenges and Future Directions. Paper presented at 1st International Workshop on Responsible AI (RAI) for Healthcare and Net Zero, IIT Madras, Chennai, India

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

Ensemble Supervised Learning-based Approaches for Mobile Network Coverage and Quality Predictions in a University Setting

Okiemute Osiezagha, M., Kumar Mishra, B., & Fagbola, T. M. (2024, August). Ensemble Supervised Learning-based Approaches for Mobile Network Coverage and Quality Predictions in a University Setting. Paper presented at International Conference on Intelligent Systems with Applications in Communications, Computing and IoT (ICISCCI-2K24), Vardhaman College of Engineering, Hyderabad, India

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

* Biomedical and public health applications

* Fairness and ethical application of ML

* Multi-Agent LLM Systems and Applications

* Domain-specific Small Language Models

* Medical Image Processing

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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