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Dr Ahmad Lawal

Job: Lecturer in Data Analytics

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

Address: De Montfort University, The Gateway, Leicester, LE1 9BH

T: N/A

E: ahmad.lawal@dmu.ac.uk

 

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Research group affiliations

  • Institute of Artificial Intelligence (IAI)
  • Digital Future Institute

Publications and outputs

  • Lawal, A., Yang, Y., Baisa, N. L., & He, H. (2026). Reservoir permeability prediction using Integrated Grey-Fuzzy Gaussian Process Regression: A comprehensive framework for uncertainty quantification and interpretability. Engineering Applications of Artificial Intelligence, 177, 114777.
  • Pasbanigoloojeh, R., Lawal, A. [Asmau], Daneshvar, B., & Lawal, A. (2025). Anisotropic Perlin Noise: Directional and Radial Pattern Generation for Texture Synthesis. In 2025 IEEE 17th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 1284–1288). IEEE.
  • Bilbis, A. L., Daneshvar, B., Lawal, A., & Pasbanigoloojeh, R. (2025). DCT vs. FFT Frequency Features for Compression-Robust Deepfake Detection: A Systematic Comparison. In 2025 IEEE 17th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 1681–1685). IEEE.
  • Lawal, A., Daneshvar, B., Bilbis, A. L., & Pasbanigoloojeh, R. (2025). Grey-Fuzzy Enhanced Gaussian Process Regression for Concrete Strength Prediction and Reliability Assessment. In 2025 IEEE 17th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 2208–2212). IEEE.
  • Lawal, A., Yang, Y., He, H., & Baisa, N. L. (2024). Machine Learning in Oil and Gas Exploration — A Review. IEEE Access.
  • Lawal, A., Yang, Y., Baisa, N. L., & He, H. (2024). A novel framework for reservoir permeability prediction using GPR with grey relational grades and uncertainty quantification. In 2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI) (pp. 404–411). IEEE.
  • Lawal, A., Yang, Y., Baisa, N. L., & He, H. (2024). A novel fuzzy logic framework for model reliability evaluation in permeability prediction using GPR. In 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 1196–1207). IEEE.
  • Lawal, A., Yang, Y., Baisa, N. L., & He, H. (2024). Uncertainty-aware reservoir permeability prediction using Gaussian Processes Regression and NMR measurements. In Proceedings of the 2024 8th International Conference on Advances in Artificial Intelligence (pp. 54–60).
  • Lawal, A., Yerima, S. Y., Olago, D. O., Amingo, P. O., Kariuki, C. W., Wang'ombe, W., Olaka, L., Obiero, L., & Wandiga, S. O. (2023). Evaluating machine learning models for rainfall prediction: A case study of Nyando in Kenya. In 2023 IEEE 15th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 264–271). IEEE.

Research interests/expertise

  • Trustworthy and Explainable AI
  • AI for Cyber Security
  • Uncertainty Quantification in Machine Learning
  • Machine Learning for Engineering and the Geosciences
  • AI for Climate and Environmental Applications

Areas of teaching

  • Computer Science
  • Software Engineering
  • Data Analytics
  • Applied Computing

Qualifications

PhD - Computer Science (Machine Learning)
Msc - Software Engineering
Bsc - Mathematics

Membership of professional associations and societies

Fellow of Higher Education Academy (FHEA)

Projects

ADRELO Project at De Montfort University, under Belmont Forum (Research Fellow)

Conference attendance

IEEE CICN 2023, IEEE CICN 2024, PRAI 2024, ICAAI 2024, IEEE CICN 2025

ORCID number

0009-0004-4397-3045