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Mr. Diluka Wijesinghe

Lecturer Probationary, Course Director – University of Plymouth Computing Degree Programmes

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Mr. Diluka Wijesinghe is a Lecturer (Probationary) and Course Director of the Plymouth Computing Degree Programmes at the Faculty of Computing, NSBM Green University. He holds a Bachelor of Science (Honours) in Computer Science with First Class Honours from NSBM Green University, in affiliation with the University of Plymouth, United Kingdom, and a Master of Science in Computer Science, specialising in Artificial Intelligence, from the University of Sri Jayewardenepura.

His professional experience spans software development, automation engineering, higher education, academic coordination, and computing education. In addition to his teaching and academic leadership responsibilities, he serves as the Faculty Coordinator for the Plymouth Computing Degree Programmes, contributing to programme development, student engagement, and extracurricular initiatives that enrich the student learning experience.

His research interests encompass artificial intelligence, machine learning, multimodal deepfake detection, computational sustainability, and trustworthy digital systems. His work focuses on the design, implementation, and empirical evaluation of intelligent systems, with particular emphasis on multimodal evidence analysis, automated machine learning, systematic benchmarking, and emerging digital verification technologies. He is especially interested in advancing responsible, computationally efficient, and practically deployable AI solutions that address contemporary challenges in digital authenticity, system reliability, and technological trust.

Research Interests

  • Artificial Intelligence and Machine Learning
  • Deep Learning and Automated Machine Learning
  • Multimodal Deepfake Detection and Digital Media Forensics
  • Computational Sustainability and Energy-Efficient AI
  • Large Language Models and AI-Generated Source Code Evaluation
  • Explainable, Trustworthy, and Responsible Artificial Intelligence
  • Blockchain-Based Digital Verification and Data Integrity
  • Anomaly Detection and Ensemble Machine Learning
  • AI for Digital Evidence Integrity and Forensic Applications
  • Sustainable Software Engineering and Empirical Software Evaluation

Conference (Full Paper) Publications

  1. Wijesinghe, D.T. and Rajasinghe, R.D. (2026). “Evaluating the Computational Sustainability of Large Language Model-Generated Python Programs: A Multi-Dimensional Benchmarking Framework.” In 2026 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), pp. 266–272. IEEE.
  2. Wijesinghe, C. and Wijesinghe, D.T. (2026). “Unsupervised Anomaly Detection of Rare Transients in the ZTF Bright Transient Survey via Heterogeneous Model Ensemble.” In 2026 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), pp. 140–146. IEEE.
  3. Rajasinghe, R.D. and Wijesinghe, D.T. (2026). “A Federated Multimodal Deepfake Detection Framework for Enhancing the Integrity of Video Evidence in Court Systems.” In 2026 9th International Conference on Electronics Technology (ICET), pp. 806–811. IEEE.
  4. Wijesinghe, D. (2023) ‘Model ML: An Automated Platform for Precise and Efficient Machine Learning Model Development’, in 5th International Research Conference. Trincomalee Campus, Eastern University, 2023.

Books and Book Chapters

  1. Wijesinghe, D.T. and Perera, G. (2025). “Unlocking Machine Learning Potential—Simplified Automated Model Development.” In Transformative Applied Research in Computing, Engineering, Science and Technology, pp. 182–187. CRC Press. DOI: 10.1201/9781003616368-24

On-Going Research

  1. Energy Efficiency of AI-Generated Source Code:
  2. A Computational Framework for Buddhist-Inspired Artificial Intelligence

Research and Project Contributions to the Sustainable Development Goals (SDGs)

His research and other project contributions are aligned with the following SDG(s):