Dr. Eng. Manal Hassan Amin Abdelrahman is a Digital Transformation Leader and PhD Researcher at University of York specializing in AI/ML, Cloud Infrastructure, Digital Twins, and SCADA systems for industrial and energy sectors. With over 15 years of experience in Oil & Gas automation and EPCM delivery at GASCO, she develops scalable edge-cloud platforms integrating Azure DevOps, Terraform, Kubernetes, and distributed ML pipelines for real-time industrial analytics.
Manal Hassan Amin Abdelrahman is a highly accomplished Digital Transformation Integrator, AI/ML Specialist, and Cloud Infrastructure Engineer with more than 15 years of experience in industrial automation, SCADA systems, digital platforms, and cloud-native technologies within the Oil & Gas sector. She currently serves as a PhD Researcher in Digital Platforms Technologies and Teaching Assistant in Computer Science and Electronics at University of York, where her research focuses on intelligent distributed systems, Digital Twins, and AI-driven edge-cloud infrastructures for industrial applications.
She is the creator of Distributed Digital Twins (DDTwins), an advanced analytical platform designed to deploy machine learning pipelines and CI/CD workflows across distributed edge computing environments for real-time industrial data analysis, forecasting, optimization, and intelligent operational control. She also developed MsFlow, a self-optimized ML workflow pipeline that enables adaptive orchestration, deployment, and optimization of AI services across edge-cloud infrastructures using microservices, reinforcement learning concepts, and automated CI/CD methodologies to enhance scalability, resilience, and resource efficiency.
Her technical expertise spans Cloud Infrastructure, DevOps, Infrastructure as Code (Terraform), Kubernetes, Docker, Flask-based Microservices, AI/ML pipelines, Digital Twins, distributed edge computing, and cloud-native architectures. She has extensive experience designing and deploying scalable cloud infrastructures using technologies such as AKS, Azure Functions, Azure SQL Database, ARM templates, storage accounts, and web applications, fully integrated through automated CI/CD pipelines using Azure DevOps and Terraform. She is also highly proficient in automation scripting using Python, Bash, and PowerShell, enabling efficient deployment, monitoring, auditing, and operational optimization of complex distributed systems.
Alongside her academic research, Manal has held senior engineering and leadership positions at GASCO, where she successfully delivered large-scale EPCM projects for SCADA systems, telecom infrastructure, fiber optics, UPS systems, solar systems, and industrial automation across major national Oil & Gas projects in Egypt. Her expertise includes real-time monitoring and control systems, Distributed Control Systems (DCS), PLC/RTU programming, SCADA database administration, industrial networking, and communication protocols including Modbus TCP, MQTT, DNP3, and TCP/IP. She has also contributed to operational efficiency improvements and cost reduction initiatives through intelligent automation strategies and AI-driven industrial analytics.
In addition to her engineering and research contributions, Manal actively supports teaching and academic development at the University of York by assisting undergraduate students in Computer Science and Electronics through laboratory instruction, tutorials, coursework guidance, and practical training in Python, C/C++, embedded systems, digital systems, and software engineering principles.
Her current research interests include Distributed Digital Twins, self-optimized AI/ML workflows, adaptive edge-cloud orchestration, CI/CD automation for ML services, Virtual Network Functions (VNFs/CNFs), intelligent industrial infrastructure, and AI-enabled autonomous systems for critical industrial environments. Driven by innovation and continuous improvement, Manal is passionate about developing resilient, scalable, and future-ready digital platforms that bridge industrial operations with AI-driven cloud and edge technologies.