Abd AlRahman Rasheed AlMomani, PhD
Information Theory · Causal Discovery · Complex Systems
Assistant Professor of Data Science & Mathematics
Embry-Riddle Aeronautical University
Finding the structure that makes complex systems understandable.
My path through Mechatronics Engineering, Applied Mathematics, and Electrical and Computer Engineering shapes how I approach complex systems. I develop information-theoretic methods for causal discovery, interpretable modeling, and network analysis, especially when data is noisy, sparse, incomplete, or contaminated by outliers. I also design global optimization methods for difficult industrial decision problems.
About
I solve complex-systems problems with the mathematics that makes models trustworthy. My work draws on information theory, entropy, and causality to build models that are interpretable, identifiable, and robust, even when the data is noisy, sparse, limited, or has missing features.
Two threads run through my work. The first is information-theoretic: geometric and entropy-based frameworks that separate informative signal from noise, uncover causal structure, and recover the governing relationships behind data. The second is optimization: I design global, derivative-free methods (genetic algorithms, particle-swarm optimization, and related metaheuristics) to solve hard industrial decision problems such as production scheduling, resource and power allocation, and system design.
This research has been supported over the years by AFRL, NSF, ONR, and ARO, and at its core it remains a search for the structure that makes complex systems understandable.
Education
Clarkson University, NY
Electrical & Computer Engineering, 2017–2019
Research supported by ARO
Prediction Analysis and System Identification of Complex Systems
Clarkson University, NY
Applied Mathematics, 2015–2017
Research supported by ONR
Coherence from Video Data without Trajectories
Philadelphia University, Jordan
Mechatronics Engineering, 2006–2010