Dr Salman Ahmed

Salman is a Fellow of the Higher Education Academy (FHEA) and contributes to undergraduate and postgraduate teaching at the University of Suffolk alongside his Research Fellow role through a Lecturer secondment. His teaching interests include programming, artificial intelligence, machine learning, data science, predictive analytics, responsible AI, blockchain and data-driven decision making. Salman also contributes to postgraduate project supervision, assessment and moderation. He has supervised PhDs and MSc Computing students. Before joining the University of Suffolk, Salman worked as a Lecturer in Computer Science at Alhamd Islamic University and as a Graduate Demonstrator at Ulster University, contributing to teaching, assessment and student project supervision.

Salman’s research focuses on the development and evaluation of artificial intelligence methods for real operational settings. His work combines machine learning, natural language processing, predictive analytics and data engineering with applications in agriculture, IT operations and other data-intensive environments. At the Digital Futures Institute, his current research includes the application of large language models, natural-language interfaces, knowledge graphs and predictive models to agricultural data and decision support. His work with industry partners has included AI-assisted forms management, natural-language querying of agricultural databases and crop-yield forecasting using commercial farm data. His earlier research in AIOps examined automated IT incident classification and severity prediction using industrial datasets. During his PhD at Ulster University, he worked with Allstate on methods for identifying and predicting major infrastructure incidents using natural language processing, machine learning and transformer-based models.

Current and Recent Research Activity

• Agri-F2P: Feasibility Project for AI-Driven Forms Management in Agriculture: Innovate UK-funded research on natural-language interaction with agricultural data and AI-assisted generation of farm documentation.
• Agri-KG: Transforming Agriculture with an AI-Powered Knowledge Graph: Innovate UK-funded research applying AI and knowledge-graph technologies to agricultural information management and decision support.
• AI-based commercial strawberry yield forecasting: Research using commercial horticultural data to evaluate machine-learning approaches for daily crop-yield forecasting.
• Scoping study on coastal erosion evidence, data and modelling approaches for Suffolk: Research contract with Suffolk Resilience Forum examining existing evidence, datasets, modelling approaches, assumptions and evidence gaps.
• Open research and research-assessment activity: Contribution to CoARA-related open research and research-assessment work, including the Open Science OER Toolkit and Open Research Assessment resource.
• AI-Driven Web Scraping and Event Data Curation: Development of automated web-scraping and AI-assisted data-curation agents that aggregate and structure public event and activity information from multiple online sources for hyper-localised recommendation services.

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’s work includes research and knowledge exchange with businesses and external organisations, with a particular focus on translating artificial intelligence research into systems that can be evaluated in operational settings. At the University of Suffolk, he has served as Co-Principal Investigator on Innovate UK-funded AgriTech projects undertaken with industry partners. His work with Diometer has involved research into artificial intelligence for agricultural data management, large language models, knowledge graphs, natural-language querying and predictive analytics.

The Agri-F2P project investigated AI-driven interaction with agricultural data and automated generation of farm documentation, while Agri-KG examined the use of AI-powered knowledge graphs to support the management and interpretation of agricultural information. His earlier industrial research included collaboration with Allstate during his doctoral research at Ulster University, where he applied natural language processing and machine-learning methods to IT incident classification, severity prediction and AIOps. Salman is interested in collaborative research, consultancy and externally funded projects involving artificial intelligence, natural language processing, data science, predictive analytics, software systems and AgriTech.

Professional Recognition and External Esteem

• Fellow of the Higher Education Academy (FHEA), Advance HE.
• Endorsed by the Royal Academy of Engineering under the UK Global Talent route as an Exceptional Talent following a peer-reviewed assessment.
• Peer reviewer for the Multiple Journals such as IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing and Soft Computing.