Biography
Dr. Firuz Kamalov is a Professor of Mathematics and Machine Learning at Canadian University Dubai, where he has been a faculty member since 2011. Recognized as a Stanford–Elsevier Top 2% Scientist, Dr. Kamalov is a distinguished academic with a prolific research portfolio comprising over 150 publications spanning mathematics, machine learning, time series analysis, and education. He holds a PhD in Mathematics from the University of Nebraska, a Graduate Certificate in Data Science from Harvard University, and a BA in Mathematics and Economics with Honors from Macalester College.
In addition to his scholarly writing, Dr. Kamalov has established a strong track record of securing high-value funding, totaling over AED 8.8 million in recent years for projects involving Medical AI and educational reform. Notable grants include work on identifying markers for dementia funded by the Dubai Future Foundation and a major project on numerical computing in mathematics curricula for the UAE Ministry of Education. A recipient of the Teaching Excellence Award, he also holds significant editorial leadership roles, currently serving as the Editor-in-Chief of the Gulf Journal of Mathematics and as an Associate Editor for the Journal of Intelligent & Fuzzy Systems.
Academic Publications
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Kamalov, F., El Sayed, A., Gurrib, I., Long, K. Q., Choi, J. Y., & Malkawi, G. (2026). Fourier-based adaptive spectral synthesis: Decision-making with imbalanced management data. Information, 17(8), Article 748. https://doi.org/10.3390/info17080748
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Nami, M., Peebles, D., Thabtah, F., & Kamalov, F. (2026). RDoC-informed explainable AI as a paradigm for multilevel Alzheimer’s disease diagnosis and progression prediction: A systematic review. Brain Informatics, 13(1), Article 28. https://doi.org/10.1186/s40708-026-00310-4
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Hendrycks, D., Mazeika, M., Zhang, O., Hausenloy, J., Ren, R., Kim, R., Khoja, A., Li, N., Gatti, A., Phan, L., Wang, A., Yue, S., Telluri, A., Wu, A., Wang, K., Nagumalli, L., Nguyen, L., Zhang, A., Saha, A., Shah, N., … Dodonov, D. (2026). A benchmark of expert-level academic questions to assess AI capabilities. Nature, 649(8099), 1139–1146. https://doi.org/10.1038/s41586-025-09962-4
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Elsayed, A. A., Malkawi, G., & Kamalov, F. (2026). Fuzzy fractional-order adaptive control for mobility-as-a-service orchestration: A cybernetic approach. IEEE Access, 14, 64459–64475. https://doi.org/10.1109/ACCESS.2026.3687256
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Santandreu Calonge, D., Kamalov, F., Medina Aguerrebere, P., Hassock, L., Smail, L., Yousef, D., Thadani, D. R., Kwong, T., & Abdulla, N. (2026). Upskilling and reskilling in the United Arab Emirates: Future-proofing careers with AI skills. Journal of Adult and Continuing Education, 32(1), 98–126. https://doi.org/10.1177/14779714251315288
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Kamalov, F., Thabtah, F., Sivaraj, R., & Abdelhamid, N. (2026). Path-sampled integrated gradients. Gulf Journal of Mathematics, 22(1). https://doi.org/10.56947/gjom.v22i1.4141
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Kamalov, F. (2026). Elliptic separator: A geometric approach to linear classification. In L. Rutkowski, R. Scherer, M. Korytkowski, W. Pedrycz, R. Tadeusiewicz, & J. M. Zurada (Eds.), Lecture Notes in Computer Science (Vol. 15949, pp. 318–328). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-03708-4_26
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Elsayed, A., Malkawi, G., Wardat, Y., & Kamalov, F. (2026). Beyond automation: A systematic review of AI teaching methodologies and a framework for human–AI synergy in higher education. IEEE Access, 14, 45948-45963. https://doi.org/10.1109/ACCESS.2026.3675937
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Kamalov, F., & Sulieman, H. (2026). From rule-based to goal-oriented: A systematic review of agentic AI in education. IEEE Access, 14, 95314–95327. https://doi.org/10.1109/ACCESS.2026.3704764
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Nazir, A., Shorfuzzaman, M., Lotfi, M. L., Kamalov, F., Badawi, S., Takruri, M., & Jallad, A.-H. (2026). Forecasting COVID-19 new cases using NBEATS deep learning and mobility data. PLOS ONE, 21(6), e0350264. https://doi.org/10.1371/journal.pone.0350264
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Santandreu Calonge, D., Smail, L., Kamalov, F., Yousef, D., & Sylvain, M. (2026). How can accessibility in computing education be improved through HCI research? In ITiCSE 2026—Proceedings of the 31st ACM Conference on Innovation and Technology in Computer Science Education V. 1 (pp. 394–400). Association for Computing Machinery. https://doi.org/10.1145/3803400.3809299
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Bogotyrev, R., & Kamalov, F. (2026). Lightweight Machine Learning-Based Intrusion Detection for Vehicle-To-Everything Communications. In 2026 International Conference on Smart Mobility, SM 2026. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/SM69703.2026.11614159
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Hooshmandi, S., Rahimi, Jaberi, K., Kamalov, F., & Nami, M. (2026). Adaptive cascading artificial intelligence for Alzheimer’s disease assessment: a clinically oriented narrative review and implementation framework. Neurological Sciences, 47(8), Article 678. https://doi.org/10.1007/s10072-026-09282-z
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Calonge, D. S., Smail, L., Kamalov, F., & Ruth, T. (2026). AI-Assisted Collaboration in Computing Education: Rethinking Teamwork Pedagogy. In 2nd International Conference on Human-AI Interaction and Experience Design, HAXD 2026 (pp. 4–11). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/HAXD70072.2026.11620836
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Adduri, S. D., Sulieman, H., Samara, F., & Kamalov, F. (2026). Enhancing PM2.5 forecasting in arid urban environments using deep learning models: a multi-station analysis from the United Arab Emirates. Frontiers in Environmental Science, 14, Article 1864507. https://doi.org/10.3389/fenvs.2026.1864507
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Academic Contributions
- Kamalov, F., Gurrib, I. & Rajab, K. (2021). Financial Forecasting with Machine Learning: Price Vs Return. Journal of Computer Science, 17(3), 251-264.
- Kamalov, F., & Leung, H. H. (2020, November). Deep learning regularization in imbalanced data. In 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) (pp. 1-5). IEEE.
- Kamalov, F., Moussa, S., Zgheib, R., & Mashaal, O. (2020, December). Feature selection for intrusion detection systems. In 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (pp. 265-269). IEEE.
- Kamalov, F.(2020). Kernel density estimation based sampling for imbalanced class distribution. Information Sciences, 512, 1192-1201.
- Kamalov, F., & Denisov, D. (2020). Gamma distribution-based sampling for imbalanced data. Knowledge-Based Systems, 207, 106368.
- Kamalov, F., Smail, L., & Gurrib, I. (2020, December). Forecasting with Deep Learning: S&P 500 index. In 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (pp. 422-425). IEEE.
- Kamalov, F., Smail, L., & Gurrib, I. (2020, December). Forecasting with Deep Learning: S&P 500 index. In 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (pp. 422-425). IEEE.
- Kamalov, F., Smail, L., & Gurrib, I. (2020, November). Stock price forecast with deep learning. In 2020 International Conference on Decision Aid Sciences and Application (DASA) (pp. 1098-1102). IEEE.
- Thabtah, F., Hammoud, S., Kamalov, F., & Gonsalves, A. (2020). Data imbalance in classification: Experimental evaluation. Information Sciences, 513, 429–441.
- Thabtah, F., Kamalov, F., Hammoud, S., & Shahamiri, S. R. (2020). Least Loss: A simplified filter method for feature selection. Information Sciences, 534, 1-15.
- Thabtah, F., Kamalov, F., & Rajab, K. (2018). A new computational intelligence approach to detect autistic features for autism screening. International Journal of Medical Informatics, 117, 112–124.
- Okash, A., Kamalov, F., Hamidi, S., Roberts, C., & Abdulnasir, S. (2020). Data mining in the time of COVID-19. PalArch’s Journal of Archaeology of Egypt / Egyptology, 17(8), 224-248.
- Kamalov, F. (2020). Forecasting significant stock price changes using neural networks. Neural Computing and Applications, 32, 17655–17667.
- Kamalov, F. (2020). Generalized feature similarity measure. Annals of Mathematics and Artificial Intelligence, 88, 987–1002.
- Kamalov, F., Leung, H.H. & Moussa, S. (2020). Monotonicity of the χ2-statistic and Feature Selection. Annals of Data Science.
- Gurrib, I., Elsharief, E., & Kamalov, F. (2020). The effect of energy cryptos on efficient portfolios of key energy listed companies in the S&P composite 1500 energy index. International Journal of Energy Economics and Policy, 10(2), 179–193.
- Gurrib, I., Kamalov, F. & Elshareif, E. (2021). Can the leading us energy stock prices be predicted using Ichimoku clouds? International Journal of Energy Economics and Policy. 11(1), 41-51.
- Gurrib, I., & Kamalov, F. (2019). The implementation of an adjusted relative strength index model in foreign currency and energy markets of emerging and developed economies. Macroeconomics and Finance in Emerging Market Economies, 12(2), 105–123.
- Kamalov, F. (2019). Sensitivity Analysis for Feature Selection. In Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 (pp. 1466–1470).