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Dr Salman Ahmed
Research Fellow Digital Futures Institute, DigiTech Centre
- s.ahmed@uos.ac.uk
- School/Directorate
- Research Directorate
- Salman Ahmed ORCID
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Salman Ahmed is a Research Fellow at the DigiTech Centre within the Digital Futures Institute at the University of Suffolk. His work is integral to the Centre’s mission, contributing significantly to industrial consultancies, training initiatives, and knowledge dissemination. With a strong commitment to mentoring students, Salman fosters a research-driven environment that encourages innovation and academic growth.
Salman holds a Ph.D. in Data Analytics, with a specialization in Natural Language Processing (NLP), from Ulster University. He also earned a Master's degree in Software Engineering and a Bachelor's degree in Information Technology, equipping him with a broad and solid foundation in both theory and application.
During his doctoral studies at the Intelligent Systems Research Centre (ISRC) within Ulster University’s School of Computing, Engineering, and Intelligent Systems, Salman led a significant industrial NLP project supported by the UK Research and Innovation Turing AI Fellowship and research work was funded by the Engineering and Physical Sciences Research Council (EPSRC), this project developed an advanced multi-modal analytics approach to predict major infrastructure incidents for Allstate. His work was pivotal in devising preventative measures, underscoring his commitment to practical, impactful research.
Salman’s expertise includes the application of state-of-the-art transformer models such as BERT, ERNIE, and RoBERTa. His portfolio spans a range of collaborative projects between industry and academia, including those under the UKRI research framework. His research contributions are well-recognized, with publications in prestigious conferences and journals, particularly in areas like AIOps, Text/Dialogue Summarization, Sentiment Analysis, Social Media Analytics, Scientific Document Processing, and Financial/Forensic Sciences.
At the University of Suffolk, Salman has continued to demonstrate his research prowess, securing two UKRI grants totalling approximately £350,000 as a co-investigator on Intelligent Agritech projects. These projects have not only advanced academic research but have also spurred further funding, business growth, and job creation, reinforcing his role as a driving force in both academia and industry.
Salman is known for his collaborative nature. He actively engages with colleagues to foster a productive and innovative research environment. His dedication to academic excellence is evident in his meticulous approach to managing projects and meeting objectives within set timelines. Salman’s multidisciplinary expertise and unwavering commitment to advancing the field of Data Analytics and NLP make him an invaluable asset to the DigiTech Centre and the broader research community.
Before starting his Ph.D. studies in 2020, Salman held a position as a Lecturer in the Department of Computer Science at Alhamd Islamic University, Pakistan. During this period, he was responsible for teaching undergraduate courses in computer programming, software design, and development, and he supervised several final-year projects. While pursuing his Ph.D., Salman also served as a teaching assistant at Ulster University, where his duties included tutoring, preparing course materials, grading, and co-supervising MSc students' projects. Recently, he was awarded the Fellowship of the Higher Education Academy (FHEA), under Reference # PR293498, in recognition of his contributions to teaching and learning in higher education. These experiences have provided him with strong preparation for a future career in academia in the UK.
Conference proceedings
Ahmed, Salman, M. Singh, S. Bhattacharyya, and D. Coyle, “Decoding neural activity for part-of-speech tagging (pos),” in 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2023, pp. 3079–3084. doi: 10.1109/SMC53992.2023.10394253.
Ahmed, Salman, M. Singh, B. Doherty, et al., “Knowledge-based intelligent system for it incident devops,” in 2023 IEEE/ACM International Workshop on Cloud Intelligence AIOps (AIOps), 2023, pp. 1–7. doi: 10.1109/AIOps59134.2023.00005.
Salman Ahmed, M. Singh, M. Bucholc, and D. Coyle, “An evaluation of bert applied for aiops,” English, The First UK AI Conference 2023 - Turing AI Fellowship Event ; Conference date: 24-05-2023 Through 25-05-2023, May 2023. url: https://uk-ai.org/ukai2023/.
Ahmed, Salman, M. Singh, B. Doherty, E. Ramlan, K. Harkin, and D. Coyle, “Ai for information technology operation (aiops): A review of it incident risk prediction,” in 2022 9th International Conference on Soft Computing Machine Intelligence (ISCMI), 2022, pp. 253–257. doi: 10.1109/ISCMI56532.2022.10068482.
Ahmed, Salman, M. Singh, B. Doherty, E. Ramlan, K. Harkin, and D. Coyle, “Multiple severity-level classifications for it incident risk prediction,” in 2022 9th International Conference on Soft Computing Machine Intelligence (ISCMI), 2022, pp. 270–274. doi: 10.1109/ISCMI56532.2022.10068477.
A. Butt, N. Javaid, S. Mujeeb, Ahmed, Salman, M. M. S. Ali, and W. Ali, “Foged energy optimization in smart homes,” in Innovative Mobile and Internet Services in Ubiquitous Computing: Proceedings of the 12th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2018), Springer, 2019, pp. 265–275.
Naseem, M. Anwar, Ahmed, Salman, A. Jan, and A. K. Malik, “Reusing stanford pos tagger for tagging urdu sentences,” in 2017 13th International Conference on Emerging Technologies (ICET), IEEE, 2017, pp. 1–6.
Journal Articles
A. Mohamed Mohideen, M. S. Nadeem, J. Hardy, et al., “Behind the code: Identifying zero-day exploits in wordpress,” Future Internet, vol. 16, no. 7, 2024, issn: 1999-5903. doi: 10.3390/fi16070256.
Ahmed, Salman, M. Singh, B. Doherty, et al., “An empirical analysis of state-of-art classification models in an it incident severity prediction framework,” Applied Sciences, vol. 13, no. 6, 2023, issn: 2076-3417. doi: 10.3390/app13063843.
S. A. K. Gahyyur, A. Razzaq, S. Z. Hasan, Ahmed, Salman, and R. Ullah, “Evaluation for feature driven development paradigm in context of architecture design augmentation and perspective implications,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 3, pp. 236–247, 2018.
S. A. K. Ghayyur, Ahmed, Salman, M. Ali, A. Razzaq, N. Ahmed, and A. Naseem, “A systematic literature review of success factors and barriers of agile software development,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 3, pp. 278–291, 2018.
S. A. K. Ghayyur, Ahmed, Salman, A. Naseem, and A. Razzaq, “Motivators and demotivators of agile software development: elicitation and analysis," International Journal of Advanced Computer Science and Applications, vol. 8, no. 12, pp. 304–314, 2018.
S. A. K. Ghayyur, Ahmed, Salman, S. Ullah, and W. Ahmed, “The impact of motivator and demotivator factors on agile software development the case of Pakistan,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 7, pp. 80–93, 2018.
S. A. K. Ghayyur, A. Razzaq, S. Ullah, and Ahmed, Salman, “Matrix clustering based migration of system application to microservices architecture,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 1, pp. 284–296, 2018.
A. Khan, F. Bibi, M. Dilshad, Ahmed, Salman, Z. Ullah, and H. Ali, “Accident detection and smart rescue system using android smartphone with real-time location tracking,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 6, pp. 341–355, 2018.
A. Khan, N. Javaid, A. Naseem, et al., “Game theoretical demand response management and short-term load forecasting by knowledge based systems on the basis of priority index,” Electronics, vol. 7, no. 12, p. 431, 2018.
A. Naseem, M. Anwar, Ahmed, Salman, Q. A. Satti, F. R. Hashmi, and T. Malik, “Tagging Urdu sentences from English pos taggers,” International Journal Of Advanced Computer Science And Applications, vol. 8, no. 10, pp. 231–238, 2017.
Salman has demonstrated a strong capacity for business engagement and enterprise through securing two Innovate UK (UKRI) funding grants as a Co-Principal Investigator, totaling £350,000 in collaboration with an industrial partner. The first project, titled "Agri-KG: Transforming Agriculture with an AI-Powered Knowledge Graph," focuses on revolutionizing agricultural practices by integrating AI-driven knowledge graphs to enhance decision-making processes. The second project, "Agri-F2P: Feasibility Project for AI-Driven Forms Management in Agriculture," explores the use of AI to streamline and optimize the management of agricultural data and forms.
In addition to his academic achievements, Salman brings extensive experience in managing multinational companies. Recently, he played a key role in a data analytics project with Ulster University and AllState Inc., developing an IT Incident Severity Prediction Framework. This project was supported by the UK Research and Innovation Turing AI Fellowship, funded by the Engineering and Physical Sciences Research Council, showcasing his ability to bridge academic research with real-world industry needs.