Dr. Mohamed Hegab | Scheduling Automation | Best Researcher Award

Dr. Mohamed Hegab | Scheduling Automation | Best Researcher Award

Dr. Mohamed Hegab | Professor | California State University | United States

Academic Background

Dr. Mohamed Hegab holds a PhD in Civil Engineering and is a licensed Professional Engineer with certifications in Project Management and Construction Management. His academic journey encompasses extensive training and research in infrastructure systems, project controls, and construction technology. With over three decades of experience in both academia and industry, he has contributed to advancing knowledge in construction planning, public-private partnerships, and AI-enabled construction automation. His scholarly impact is demonstrated through a robust portfolio of publications, books, and peer-reviewed research Citation Index: Google Scholar Citations ≈ 480 | h-index = 11 | i10-index = 12 reflecting his influence in the field. All supporting documents and credentials are verifiable upon request.

Research Focus

Dr. Hegab’s research centers on integrating artificial intelligence with construction planning and management. His work focuses on ontology-based frameworks for automated scheduling, digital twin integration, and smart infrastructure monitoring. He explores innovative approaches to construction productivity modeling, risk assessment, and project controls that bridge academic theory with industry practice.

Work Experience

Dr. Hegab has served as a Professor and Department Chair, leading civil engineering and construction management programs. His professional experience spans consulting for large-scale infrastructure projects, including metropolitan water systems and state transportation authorities. He has overseen multi-disciplinary teams, managed project budgets, and provided expert advisory services to public and private organizations. Beyond academia, he has held leadership positions in businesses supporting construction operations, demonstrating a unique blend of academic rigor and practical expertise.

Key Contributions

Dr. Hegab has pioneered the use of AI-driven semantic frameworks in construction planning, enabling automated project scheduling and constraint validation. His work has improved decision-making processes, minimized data fragmentation in digital models, and enhanced the implementation of Construction 4.0 practices. He has significantly influenced industry standards, academic curricula, and international research collaborations, bridging the gap between emerging technologies and practical infrastructure delivery.

Awards & Recognition

Dr. Hegab has been widely recognized for his contributions to construction engineering and management. His research and industry leadership have garnered national and international attention, earning accolades for innovation in project delivery, risk assessment, and AI integration in construction processes. His work continues to inspire academic peers and industry professionals globally.

Professional Roles & Memberships

Dr. Hegab actively contributes to professional organizations, including the American Society of Civil Engineers, Construction Management Association of America, Project Management Institute, and the Dispute Resolution Board Foundation. He serves as a senior evaluator for accreditation bodies and participates in multidisciplinary research collaborations with universities and research institutions worldwide, supporting the advancement of construction engineering education and practice.

Publication Profile

Scopus | Google Scholar

Featured Publications

Hegab, M., Smith, G. R. (2007). Delay time analysis in microtunneling projects. Journal of Construction Engineering and Management, 133, 191-195.

Nassar, K., Gunnarsson, H. G., Hegab, M. (2005). Using Weibull analysis for evaluation of cost and schedule performance. Journal of Construction Engineering and Management, 131, 1257-1262.

Hegab, M., Salem, O. M. (2010). Ranking of the factors affecting productivity of microtunneling projects. Journal of Pipeline Systems Engineering and Practice, 1, 42-52.

Ali, S., Zayed, T., Hegab, M. (2007). Modeling the effect of subjective factors on productivity of trenchless technology. Journal of Construction Engineering and Management, 133, 743-748.
Elwakil, E., Hegab, M. (2018). Risk management for power purchase agreements. IEEE Conference on Technologies for Sustainability, 1-6.

Impact Statement / Vision

Dr. Hegab envisions a future where AI-driven methodologies and digital integration transform construction management, enabling smarter, safer, and more efficient infrastructure systems. His work continues to advance knowledge, inform policy, and inspire innovation across academia and industry globally.

JINHO CHA | Operations Management | Best Researcher Award

Dr. JINHO CHA | Operations Management | Best Researcher Award

Dr. JINHO CHA , Adjunct Faculty, Gwinnett Technical College, United States.

Dr. Jinho Cha is an accomplished academic and researcher with a strong foundation in industrial engineering and computer science. He currently serves as an Adjunct Faculty member in the Department of Computer Science at Gwinnett Technical College, GA, USA. With a Ph.D. from Clemson University, his career spans teaching, research leadership, and applied data science across military and academic sectors. His research projects focus on leveraging AI, smart contracts, and data-driven optimization to address real-world problems. Dr. Cha’s cross-disciplinary expertise bridges technical innovation and practical application, significantly contributing to educational innovation, defense analytics, and public health systems.

Publication Profile

Scopus

ORCID

Google Scholar

🎓 Education Background

Dr. Cha holds a Ph.D. in Industrial Engineering from Clemson University (2015), where his dissertation focused on truncated normal distributions. Prior to this, he earned two master’s degrees—one in Industrial Engineering (Operations Research) from the University of Florida (2012), and another in Industrial Engineering (Quality & Statistics) from Texas A&M University (2007). His academic journey began at the Korea Military Academy, where he earned his B.S. in Electronic Engineering in 1999. This diverse educational foundation underpins his strong analytical skills and interdisciplinary approach in solving complex engineering and data challenges.

💼 Professional Experience

Dr. Cha currently serves as an Adjunct Faculty in Computer Science at Gwinnett Technical College, where he also leads innovative research programs. Previously, he was a Data Scientist and analytics leader at the Korea Research Institute for Defense Technology, applying AI and statistical methods to military logistics and R&D. From 2017 to 2021, he was an Assistant Professor at the Korea Military Academy, delivering mathematics and engineering courses with top-rated evaluations. Earlier, he was a Teaching Assistant at Clemson University, enhancing his instructional depth. His professional roles reflect a blend of academia, applied research, and technical leadership.

🏅 Awards and Honors

Dr. Cha has received multiple prestigious recognitions for his service and contributions. In 2025, he was honored with the Presidential Commendation for enhancing national defense capabilities. In 2023, he earned the Ministerial Commendation for his work in logistics requirements, and in 2019, the Chief of Staff Commendation acknowledged his role in Army innovation. His 2017 Chairman of the Joint Chiefs of Staff Commendation highlighted his impactful data analysis work. These accolades affirm Dr. Cha’s excellence in combining research, technology, and public service for national and educational advancement.

🔬 Research Focus

Dr. Cha’s research spans Supply Chain Management, Smart Contracts, Operations Research, and Machine Learning, with deep specialization in statistical modeling under uncertainty. His current efforts include AI-driven educational platforms, healthcare policy analytics, and blockchain applications in procurement systems. His publications explore topics like airline scheduling, inverse optimization, and deep learning for diagnostics. Dr. Cha also leads open-source development to democratize computing education, especially in two-year colleges. He integrates theory with practice, using data science to innovate across industries—from national defense logistics to public health outcomes and smart education tools.

🔚 Conclusion

With a career marked by cross-sector impact and academic excellence, Dr. Jinho Cha stands out as a dynamic leader in applied research and education. He merges technical expertise with a mission-driven approach, advancing innovations in data analytics, smart systems, and digital learning environments. His contributions have garnered recognition at the national level and left a significant mark on military analytics, computer science education, and global health research. Dr. Cha’s interdisciplinary reach and vision for equitable access to technology underscore his value as both a scholar and a changemaker in the digital age.

📚 Top Publications 

  1. Mobile Banking Customer Satisfaction and Loyalty: The Roles of Technology Readiness
    Journal: Journal of Risk and Financial Management, 2025
    Cited by: 2 articles

  2. A study on developing ROK ammunition container
    Journal: Journal of Korea Academia-Industrial Cooperation Society, 2024
    Cited by: 1 article

  3. A study on recognition of Korea armed forces nursing academy
    Journal: Journal of Korea Academia-Industrial Cooperation Society, 2023
    Cited by: 0 articles

  4. Methodology of AI learning model construction for intelligent coastal surveillance
    Journal: Journal of Korea Internet Computing and Service, 2022
    Cited by: 3 articles

  5. Big data analysis to derive improvement direction for military mental force education
    Journal: Journal of Korea Spiritual & Mental Force Enhancement, 2021
    Cited by: 4 articles

  6. Future tactical communication system development plan through Army TIGER information distribution capability analysis
    Journal: Journal of Korea Convergence Security Association, 2021
    Cited by: 2 articles

  7. Convolutions of truncated normal and truncated skew normal distributions
    Journal: Journal of Statistical Theory and Practice, 2017
    Cited by: 15 articles

  8. Zipping and re-zipping methods to improve precision and accuracy of manufacturing processes
    Journal: International Journal of Experimental Design and Process Optimization, 2015
    Cited by: 9 articles

  9. Rethinking the truncated normal distribution
    Journal: International Journal of Experimental Design and Process Optimization, 2013
    Cited by: 18 articles