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Dr. Abd AlRahman AlMomani is an Assistant Professor of Data Science and Mathematics and the Data Science Program Coordinator. He holds a Ph.D. in Electrical and Computer Engineering from Clarkson University, an M.S. in Applied Mathematics and a B.S. in Mechatronics Engineering. His academic training integrates engineering, applied mathematics and computational modeling.

Dr. AlMomani's research focuses on information-theoretic and data-driven approaches to system identification, causal inference and modeling of complex systems. His scholarly work bridges theoretical foundations with applied computational methods relevant to engineering and data science applications. He teaches undergraduate and graduate courses in data science, statistics, linear algebra and differential equations, emphasizing computational proficiency, mathematical rigor and application-driven learning outcomes.

In his role as Program Coordinator, Dr. AlMomani contributes to curriculum development, assessment alignment and continuous program improvement, supporting accreditation standards and the long-term growth of data science education at Embry-Riddle.

PUBLICATIONS

Journal Articles

• Diggans, C. T., and AlMomani, A. A. R. (2025). Generalizing geometric partition entropy for the estimation of mutual information in the presence of informative outliers. Chaos: An Interdisciplinary Journal of Nonlinear Science, 35(3), 033141.
• Diggans, C. T., and AlMomani, A. A. R. (2023). Boltzmann–Shannon interaction entropy: A normalized measure for continuous variables with an application as a subsample quality metric. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(12). Editor’s Pick.
• Diggans, C. T., and AlMomani, A. A. R. (2022). Geometric partition entropy: Coarse-graining a continuous state space. Entropy, 24(10), 1432.
• Sun, J., AlMomani, A. A. R., and Bollt, E. (2022). Data-driven learning of Boolean networks and functions by optimal causation entropy principle. Patterns, 3(11).
• AlMomani, A. A. R., and Bollt, E. (2021). An early warning sign of critical transition in the Antarctic ice sheet: A data-driven tool for a spatiotemporal tipping point. Nonlinear Processes in Geophysics, 28(1), 153–166.
• Diggans, C. T., Fish, J., AlMomani, A. A. R., and Bollt, E. (2021). The essential synchronization backbone problem. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(11).
• Fish, J., DeWitt, A., AlMomani, A. A. R., Laurienti, P. J., and Bollt, E. (2021). Entropic regression with neurologically motivated applications. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(11).
• AlMomani, A. A. R., and Bollt, E. (2020). Go with the flow on Jupiter and snow: Coherence from model-free video data without trajectories. Journal of Nonlinear Science, 30, 2375–2404.
• AlMomani, A. A. R., Sun, J., and Bollt, E. (2020). How entropic regression beats the outliers problem in nonlinear system identification. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(1). Editor’s Pick.

Refereed Conference Proceedings

• Merlos, C., Hussain, F., Kar, S., Shri, L., Olugbenle, O., Banavar, M., and AlMomani, A. A. R. (2024). Comparison of large language models for applied mathematics questions in engineering courses. IEEE Frontiers in Education Conference, Washington, DC.
• AlMomani, A. A. R., Argyriou, A., Erol-Kantarci, M., et al. (2016). A heuristic approach for overlay content-caching network design in 5G wireless networks. IEEE Symposium on Computers and Communication, 621–626.
• AlMomani, A. A. R., Abu, Q. J. E., and Yamamoto, H. (2006). Development of a new encoding method for complex production systems. International Symposium on Scheduling, 94–99.​