People

Dr Muhammad Ali

Postdoctorol Research Associate

Profile for Dr Muhammad Ali

School/Department: Computing and Mathematical Sciences, School of

Telephone: +44 (0)758 851 3428

Email: ma909@leicester.ac.uk

Address: University Road, School of Computing and Mathematics, University of Leice

Profile

I am a Postdoctoral Research Associate at the University of Leicester, working on the AI4NetZero project funded by UK Research and Innovation (UKRI). This project focuses on reducing greenhouse gas emissions from UK agricultural land, responsible for around 11% of national emissions, through self-learning digital twins.

I hold a PhD in Computer Science from the University of Leicester, where my thesis on Edge-Enhanced Real-Time Video Stream Analytics advanced distributed deep neural networks for high-performance edge and cloud computing. My research interests include digital twins, high-performance distributed systems, edge and cloud computing, artificial intelligence and machine learning, computer vision, intelligent agents, and the Internet of Things (IoT). I am particularly motivated by the role of computing in driving sustainability, with a focus on the environmental impact of greenhouse gas emissions.

Alongside my academic research, I bring extensive industry experience, having worked in roles spanning software engineering, system development, and applied AI solutions, including projects in smart cities, NFC technologies, and large-scale data analytics. My work has been published in leading venues such as IEEE Transactions on Cloud Computing and IEEE Transactions on Sustainable Computing, and I regularly present at international conferences including ECML-PKDD, SIGCOMM, and ICML.

I actively welcome opportunities for research collaboration, student supervision, and industry partnerships in areas aligned with my expertise. Please feel free to get in touch to discuss potential projects or collaborations.

Research

Publications

  1. Ali, M., Anjum, A., Rana, O., Zamani, A. R., Balouek-Thomert, D., & Parashar, M. (2020). RES: Real-time Video Stream Analytics using Edge Enhanced Clouds. IEEE Transactions on Cloud Computing. IF: 5.967 (85+ citations)
  2. Ali, M., Anjum, A., Yaseen, M. U., Zamani, A. R., Balouek-Thomert, D., Rana, O., & Parashar, M. (2018, May). Edge enhanced deep learning system for large-scale video stream analytics. In 2018 IEEE 2nd International Conference on Fog and Edge Computing (ICFEC) (pp. 1-10). IEEE. (85+ citations)
  3. Amaizu, M.U., Ali, M., Anjum, A., Liu, L., Liotta, A., & Rana, O. (2023). Edge-enhanced QoS aware compression learning for sustainable data stream analytics. IEEE Transactions on Sustainable Computing.
  4. Aslam, M., Ali, M., Ahsan, S., Arshad, M. J., Farooq, A., & Shahbaz, M. (2012). Cross-Platform Service for Nomadic Devices in Biodiversity Research. Life Science Journal, 9(1).
  5. Hill, R., Devitt, J., Anjum, A., & Ali, M. (2017, June). Towards in-transit analytics for industry 4.0. In 2017 IEEE International Conference on Internet of Things (iThings), GreenCom, CPSCom, SmartData (pp. 810-817). IEEE.
  6. Ndubuaku, M. U., Ali, M. K., Anjum, A., Liotta, A., & Reiff-Marganiec, S. (2020). Edge-enhanced analytics via latent space dimensionality reduction. In IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), Leicester, UK, pp. 87-95. doi:10.1109/BDCAT50828.2020.00018.
  7. Rana, O., Shaikh, M., Ali, M., Anjum, A., & Bittencourt, L. (2018, August). Vertical Workflows: Service Orchestration Across Cloud & Edge Resources. In 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud) (pp. 355-362). IEEE.
  8. Ali, M., & Anjum, A. (2025). LEDIA: Edge-Enhanced Deep Learning Inference Acceleration using Continuously Adaptive Decomposition. (Submitted and under review: IEEE Transactions on Systems, Man, and Cybernetics: Systems)
  9. Khana, A., Ali, M., Kaduka, J., & Balzter, H. (2025). Upscaling CO2 fluxes from UK’s lowland agricultural peatlands using optical satellite imagery. (Accepted in Remote Sensing of Environment)
  10. Burian, A., Nielsen, J. M., Ali, M., Riekenberg, P., & Hedberg, P. (2025). Tracing pelagic algae based on their metabolic imprints on amino acid stable isotopes. (Due Submission: Scientific Advances)

Publications

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