Applied Intelligence for Safer, More Resilient Systems

With more than 15 years of combined academic and applied industry experience, his work spans the oil and gas, aviation, aerospace, and manufacturing sectors. His professional perspective has been shaped by cross-cultural leadership and practice in the United States, Canada, and Iran, giving him a broad understanding of how safety, risk, technology, and organizational systems interact across diverse regulatory and operational environments.

Dr. Zarei has authored more than 200 peer-reviewed publications in leading international journals in safety science, risk engineering, occupational safety and health, and related disciplines. He also contributes to the advancement of the field through editorial leadership and active engagement with emerging research in system safety, risk science, human factors, and responsible AI.

He holds doctoral degrees in Occupational Safety and Health Engineering and Oil and Gas Engineering. His work bridges scientific research, professional practice, education, and innovation, with a particular focus on AI-enabled risk intelligence, intervention design, control assurance, and resilient work systems.

Dr. Zarei is also an Associate Professor of Safety Science at Embry-Riddle Aeronautical University. His academic appointment is separate from his ownership, management, and professional activities with Risk Analytics AI. Risk Analytics AI operates independently and is not affiliated with, sponsored by, or endorsed by Embry-Riddle Aeronautical University.

He is a researcher in the Department of Systems Engineering at École de technologie supérieure in Montréal, Québec. His work bridges process safety, human factors, reliability engineering, and computational intelligence, with a particular focus on how technical failures, organizational conditions, and human-system interactions combine to shape accident pathways and systemic vulnerability.

Mr. Gholamizadeh develops dynamic and data-informed risk models using methods such as Dynamic Bayesian Networks, fuzzy logic, machine learning, multi-criteria decision analysis, and probabilistic modeling. These approaches support the analysis of evolving accident scenarios, interdependent hazards, uncertainty, and the performance of preventive and mitigative controls.

His research includes domino-effect analysis, hazardous-materials transportation safety, dynamic risk assessment, and resilience in high-hazard systems. He also contributes to the development of knowledge-driven governance approaches that connect occupational safety and health indicators, system reliability, and predictive AI analytics to strengthen risk intelligence and control assurance.

Through his role with Risk Analytics AI, Mr. Gholamizadeh advances the organization’s research strategy, analytical methodologies, and innovation agenda across computational risk modeling, reliability assessment, process safety, and AI-enabled risk governance.

His expertise includes cognitive and physical ergonomics, ergonomic risk assessment, human-centered design, usability evaluation, mental workload assessment, human error and system safety analysis, workflow optimization, and human-machine interaction. He applies a broad range of methods, including task and workflow analysis, participatory design, usability testing, eye tracking, EEG, physiological monitoring, biomechanical assessment, workplace observation, and mixed-methods research. He also uses applied data analysis and machine-learning approaches to translate complex human-performance and sensor data into practical design and risk-reduction recommendations.

Dr. Dehghan has worked across aviation and aerospace, healthcare, medical technology, manufacturing, petrochemical operations, occupational health, and other safety-critical environments. His current aviation work examines pilot mental workload, situation awareness, fatigue, and pilot-system interaction using multimodal biometric data such as EEG, EDA, HR/HRV, and eye tracking. His healthcare research has included IoT-enabled monitoring systems, sensor-based pain detection, and the co-creation of intelligent dashboards to support caregivers and improve decision-making in senior residences.

His professional background also includes ergonomic workplace assessment, workstation and product design, musculoskeletal disorder prevention, human error analysis, safety risk assessment, usability engineering, and the development of practical interventions for industrial and healthcare organizations. His record includes more than 35 peer-reviewed publications, 11 patents, academic leadership, applied research, technology transfer, and the design and commercialization of ergonomic products.

At Risk Analytics AI, Dr. Dehghan contributes to AI-enabled ergonomic risk assessment, human-centered intervention design, and responsible human-AI teaming. He supports organizations in identifying physical, cognitive, operational, and system-level risks associated with emerging technologies and translating those findings into practical controls, design improvements, and implementation strategies. His work is guided by a central principle: AI systems should enhance human performance and decision-making without compromising worker safety, health, autonomy, or well-being.