Amman, Jordan
I am an interdisciplinary engineer and scholar with 14+ years of experience combining civil engineering practice with computational and mathematical research. My work focuses on the development and application of rigorous mathematical models, numerical methods, and data-driven techniques to analyze complex structural, environmental, and industrial systems. With over a decade of experience, I have built a unique career at the intersection of hands-on civil engineering and advanced computational research. My expertise spans the full project lifecycle—from structural supervision and construction management of complex projects (20,000+ m², budgets up to USD 7 million) to the development of sophisticated mathematical models for optimization and predictive risk assessment. My core interests center on structural dynamics, seismic engineering, stochastic modeling, and optimization, with particular emphasis on risk-informed and sustainability-oriented decision-making. I employ artificial intelligence, statistical learning, and MATLAB-based numerical modeling to formulate predictive and optimization frameworks for engineering applications. In academic research settings, including Kabardino-Balkarian State University and in collaboration with the Russian Academy of Sciences, I investigated longitudinal vibrations in complex structural systems and developed linear and nonlinear programming (LP/NLP) models for resource allocation, system optimization, and waste minimization. At the University of Duisburg-Essen, I applied machine learning methodologies, such as regression analysis and classification trees, to integrate civil engineering and environmental sciences, with a focus on air quality assessment and sustainable resource recovery from biogenic waste streams. I am driven by the goal of developing efficient, data-driven, and environmentally sustainable solutions for the future of engineering and industry. I am keen to connect with professionals and organizations focused on innovation in smart infrastructure, risk-resilient construction, and applied industrial mathematics.
Drive operations strategy, optimization, and risk management initiatives in partnership with AGRI International LLC, translating advanced data science, machine learning, and simulation models into commercial value across production and global supply chain operations. Build and deploy integrated optimization and decision-support frameworks that strengthen profitability through strategic resource allocation, capacity planning, cost discipline, and waste reduction, while embedding risk awareness into operational execution. Leverage predictive analytics, machine learning, and scenario-based simulation to anticipate costs, model uncertainty, manage operational and market risks, and enable scalable, governance-aligned, data-driven execution in a competitive global agriculture market.
- Conducted applied research integrating civil and environmental engineering with data-driven modeling and machine learning techniques. - Completed applied research project on air quality assessment, developing predictive models using regression analysis and classification methods to evaluate pollutant behavior and health-related air quality indices. - Applied statistical learning and data analytics to support evidence-based environmental assessment and sustainability-oriented decision-making. - Initiated and developed the analytical and conceptual framework for a large-scale research project on sustainable resource recovery from biogenic waste streams, focusing on system-level dependencies, circular economy principles, and environmental impact. - Contributed to the early-stage structuring of a multi-disciplinary project linking energy systems, environmental sustainability, and industrial transformation.
Worked on the AutoStore Robot Model R5 (Red Line) from AutoStore Holdings Ltd (AUTO.OL) in a high-throughput automated warehouse, evaluating operational performance and confirming efficiency advantages over traditional outbound sorting and dispatch systems. Analyzed operational data across shifts and workforce configurations, identified nonlinear workforce–system interactions, and developed optimization strategies for human–robot interaction, workload allocation, and shift design to support management decisions in logistics automation.
- Developed and implemented advanced optimization models using Linear Programming and MATLAB to enhance production and operational efficiency - Designed models to maximize profitability through optimal resource allocation and capacity planning -Addressed nonlinear programming challenges using linearization techniques to enable practical, scalable solutions -Applied machine learning techniques (Linear Regression, Classification Trees) to: - Predict production and operational costs - Assess risks and improve decision-making - Optimize project planning and operational efficiency - Supported data-driven strategy across production and supply chain operations in a high-demand global market
Research Assistant & Interdisciplinary Engineer Kabardino-Balkarian State University (Nalchik, Russia) in collaboration with LLC PSF Magistral • Served as Research Assistant to Professor KP Kulterbaev, conducting dissertation research on “Longitudinal Vibrations of Beams with Continuously Distributed Discrete Masses under Harmonic and Random Excitations.” • Applied mathematical modeling and partial differential equations to investigate the dynamic behavior, motion, and structural response of oscillating beams under periodic and stochastic excitations, accounting for forces, damping, and external disturbances. • Developed conceptual and proposal designs for residential buildings as part of a university-industry collaboration, integrating academic research insights with practical architectural and structural engineering solutions. • Contributed at the Institute of Applied Mathematics and Automation, Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences, serving as Operations Analyst and Research Scholar, focusing on applied mathematics, optimization, risk analytics, and data-driven solutions for engineering and industrial applications.