Dr. Ahmad Al-Shami

About Dr. Al-Shami

Dr. Al-Shami is an Associate Professor of Computer Science at SAU and a distinguished recipient of multiple prestigious awards, including the SAU Faculty Excellence Award for Teaching (2023-2024) and the SAU/Science and Engineering Faculty Excellence Award for Research (2022-2023).

He earned his PhD in Computational Intelligence from Nottingham Trent University (NTU), UK in 2013, and holds a BSc. (Honors) and an MSc. in Computer Science from The City University of New York (CUNY), USA. Before joining SAU, Dr. Al-Shami gained extensive experience in academia and industry. His prior roles include Assistant Professor of Computer Information Systems and Interim CIS Division Chair at the Higher Colleges of Technology, Dubai, UAE; Senior Lecturer in Informatics at NTU; and Lecturer of Management Information Systems and Director of IT at The New York Institute of Technology (NYIT). He also has over a decade of professional experience in the financial sector on Wall Street, where he served as Manager and Director of Information Technology.

Dr. Al-Shami’s research focuses on computational intelligence and machine learning techniques, including artificial neural networks, fuzzy logic, and genetic algorithms. His applications span knowledge management, process modeling, big data mining, and strategic planning. He is an Honorary Research Fellow at the University of Warwick, UK, and has been part of significant grants, such as the NSF DART Project Grant (Award No. OIA-1946391) and the HCT Interdisciplinary Research Grant.

An accomplished scholar, Dr. Al-Shami has authored over 30 scientific papers in computational intelligence and related fields. His academic contributions include guest editing and reviewing for renowned journals and serving as a keynote speaker. He is a senior member of the IEEE and has received several accolades, including the City University of New York Honors Award and the UAE Hackathon Best Mentor Award (2020).

Dr. Al-Shami is known for his teaching excellence, consistently earning high evaluations (4.5–4.83/5.0) and supervising numerous undergraduate and graduate projects. He teaches courses such as Software Engineering, Data Structures and Algorithms, Machine Learning, Data Mining, Database Management Systems, and Discrete Mathematics.

His expertise spans:

• Computational Intelligence & Machine Learning: Artificial Intelligence (Machine Learning), Fuzzy Logic (Type I & II), Neuro-Fuzzy Systems, and Soft Computing.
• Big Data Science: Establishing Big Data infrastructures, data analytics, visualization, and compression techniques.
• Advanced Econometrics & Statistical Modeling: Multivariate Analysis, Decision Trees, Monte Carlo Simulations, and Sensitivity Analysis.

Dr. Al-Shami continues to inspire innovation and willingness to collaborate, blending cutting-edge research with impactful teaching to shape the future of Computer Science at SAU and beyond.

Selected Publications

1. 1. 1. Unified Knowledge Based Economy Hybrid Forecasting, Al Shami A, Lotfi A, Coleman S, Dostál P, Technological Forecasting & Social Change, 2015, https://dx.doi.org/10.1016/j.techfore.2014.01.014
      2. Intelligent Synthetic Composite Indicators with Application, Al Shami A, Lotfi A, Coleman S, Soft Computing, 2014, (17) 12, 2349-2364, https://link.springer.com/article/10.1007%2Fs00500-013-1098-3#
      3. Deep learning analysis of social media content: WhatsApp in focus, Elareshi, M., Ziani, A. K., Al Shami, A. (2021). Convergence, 27(2), 472-490. https://doi.org/10.1177/1354856520966914
      4. Fuzzy partition technique for clustering Big Urban dataset, Al Shami A, Guo W, Pogrebna G, IEEE, SAI Computing Conference, 2016, https://ieeexplore.ieee.org/document/7555984/
      5. Vision transformers for medical images classifications, Leamons R, Cheng H, Al Shami A., SAI Intelligent Systems Conference 2022 (pp. 319-325). Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-031-16075-2_22
      6. Unified Knowledge Economy Competitiveness Index Using Fuzzy Clustering Model, Al Shami A, Lotfi A, Coleman S, Lai E, 2011, IEEE Symposium Series on Computational Intelligence (SSCI), https://ieeexplore.ieee.org/document/5953563
      7. World Cybersecurity Indicator Using Computational Intelligence, Al Shami A. and S. Coleman, (2014), Qatar Foundation Annual Research Conference (ARC 14), https://www.qscience.com/doi/abs/10.5339/qfarc.2014.ITOP0592
      8. Ubiquitous Monitoring of Human Sunlight Exposure in Cities, Al Shami A, Guo W, Wang Y, IEEE International Smart Cities Conference (ISC2-2015), http://ieeexplore.ieee.org/document/7366210/
      9. Clustering Big Urban Dataset, Al Shami A, Guo W, Pogrebna G, IEEE International Smart Cities Conference (ISC2-2015), http://wrap.warwick.ac.uk/72290/1/WRAP_BigDataArticle_ShortV2.pdf
      10. Towards Developing 3rd Generation Intelligent Synthetic Composite Indicators, Al Shami A, Lotfi A, Coleman S, 2012, IEEE 12th Annual Workshop on Computational Intelligence (UKCI), https://ieeexplore.ieee.org/document/6335759