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Dr Amudhavel Jayavel

Job: Senior Lecturer Games and Artificial Intelligence

School/department: School of Computer Science and Informatics

Address: »ÆÉ«²Ö¿â, The Gateway, Leicester, LE1 9BH

T: 8179

E: amudhavel.jayavel@dmu.ac.uk

 

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Research group affiliations

Institute of Artificial Intelligence (IAI)

Publications and outputs

Research interests/expertise

My research focuses on advancing the fields of Computer Vision, 3D Scene Understanding, and Generative AI through innovative learning paradigms. I am particularly interested in:
  • 3D Vision and Scene Understanding – including point cloud processing, neural radiance fields (NeRF), and Gaussian splatting for realistic 3D reconstruction and rendering.
  • Computer Vision – image understanding, object detection, semantic segmentation, and depth estimation for visual intelligence and spatial reasoning.
  • Multimodal Learning – integrating vision and language models (e.g. CLIP, BLIP, Flamingo) for cross-modal representation learning and grounding.
  • Generative Adversarial Networks (GANs) – for image synthesis, style transfer, data augmentation, and domain adaptation across visual and medical imaging domains.
  • Diffusion Models – exploring next-generation generative architectures for high-fidelity image, text, and 3D content generation.

Areas of teaching

  • Computer Vision – Fundamentals of visual perception, object detection, image segmentation, and scene understanding.
  • Machine Learning – Supervised, unsupervised, and reinforcement learning algorithms with real-world applications.
  • Deep Learning – Neural network architectures (CNN, RNN, Transformer), optimization techniques, and model evaluation.
  • Generative AI – Concepts and applications of Generative Adversarial Networks (GANs), Diffusion Models, and Autoencoders.
  • 3D Vision and Graphics – Point cloud processing, depth estimation, Neural Radiance Fields (NeRF), and Gaussian Splatting.
  • Multimodal Learning – Integration of visual, textual, and auditory data for cross-modal understanding and generation
  • Parallel and Distributed Computing – CUDA programming, GPU acceleration, and parallel algorithms for AI workloads.

Qualifications

PhD in Computer Science and Engineering.

Courses taught

  • Agent-Based Modelling and Parallel Computing
  • Neural Systems
  • Generative Adversarial Networks for Computational Imaging
  • Introduction to Neuromorphic Computing
  • Reinforcement Learning
  • Artificial Neural Networks
  • Deep Learning
  • Artificial Intelligence
  • Machine Learning

Honours and awards

  • Fellow of Innovation in Science Pursuit for Inspired Research (INSPIRE)
  • University Gold Medalist

Membership of professional associations and societies

  • Lifetime Member, ISTE - Indian Society for Technical Education (ISTE)
  • Institute of Electrical and Electronics Engineers

Professional licences and certificates

  • EMC2 Academic Associate, Data Science, and Big Data Analytics
  • National Eligibility Test (UGC‑NET) for Lectureship

ORCID number

0000-0001-6227-0733