Badr Abou El Majd | Applied Mathematics | Mathematical Engineering Excellence Award

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Prof. Dr. Badr Abou El Majd | Applied Mathematics | Mathematical Engineering Excellence Award

Full Professor at Mohammed V University, Morocco

Dr. Badr Abou El Majd is a distinguished Professor of Applied Mathematics at Mohammed V University, Rabat, with extensive expertise in multidisciplinary optimization, shape design, AI-driven modeling, and computational engineering. Holding a Ph.D. from INRIA Sophia Antipolis and a Master’s from Pierre and Marie Curie University, his research spans aerospace, transportation, smart systems, and biomedical applications. He has authored over 80 peer-reviewed publications, edited scientific volumes, and holds several patents. As a leader in numerous international research projects and conferences, including EU Erasmus+ and IEEE events, Dr. Abou El Majd has demonstrated remarkable scientific leadership. His contributions to innovation, education, and cross-border collaboration are further recognized through awards like the Distinguished Scholar Award (2023–24), making him a pivotal figure in advancing research that bridges theory, technology, and real-world societal needs

Professional Profile

Google Scholar | Scopus Profile  

Education

Dr. Badr Abou El Majd holds a Ph.D. in Applied Mathematics from INRIA Sophia Antipolis – Méditerranée, France (2007), where he specialized in hierarchical algorithms and game strategies for multidisciplinary optimization, with applications in aerospace design. His doctoral work was conducted in collaboration with Dassault Aviation and Piaggio Aero, demonstrating strong industrial-academic integration. He also earned a Master’s degree in Numerical Analysis from Pierre and Marie Curie University (UPMC – Paris 6), one of Europe’s leading institutions in mathematical sciences. His early academic path was marked by a strong foundation in computational mathematics, optimization, and numerical modeling. These formative years provided the analytical and algorithmic rigor that continues to define his research contributions across disciplines such as aerospace engineering, smart systems, and data-driven decision-making.

Experience

With over two decades of academic and research experience, Dr. Abou El Majd has held prominent roles in Morocco and internationally. He is currently a Full Professor at Mohammed V University, Rabat. Previously, he served as an Associate Professor at both the International University of Rabat and Hassan II University in Casablanca. His international exposure includes visiting and research positions at INRIA Lille, University of Lille, and Polytechnique Montréal. He also worked as a scientist researcher at École Centrale Paris and completed post-doctoral research at CNRS/ENSMA. He has led and coordinated large-scale research and tech-transfer projects involving multiple institutions, government bodies, and industry leaders. This broad experience has enabled him to seamlessly bridge academic theory, industrial application, and international collaboration in fields ranging from aerospace to smart agriculture.

Research Interests

Dr. Abou El Majd’s research is deeply rooted in applied mathematics and multidisciplinary optimization, with expansive interests spanning aerodynamic shape design, robust optimization, artificial intelligence, model reduction, decision-making systems, and digital twin frameworks. He actively explores how machine learning, game theory, and numerical methods can be applied to real-world engineering and societal problems. His work spans diverse domains including aerospace design, RFID networks, cognitive radio, smart agriculture, biomedical imaging, and manufacturing optimization. Through his research, he aims to solve high-dimensional, complex optimization problems while ensuring scalability and industrial relevance. He has recently focused on surrogate modeling, AutoML, and intelligent systems for healthcare diagnostics and infrastructure planning, reflecting a strong commitment to leveraging computational science for impactful, cross-disciplinary solutions.

Award and Honor

Dr. Abou El Majd’s contributions to science and innovation have earned him several prestigious honors. Most notably, he received the Distinguished Scholar Award (2023–24) from the Arab Fund for Economic and Social Development, in collaboration with the University of Lille—an acknowledgment of his outstanding research achievements and academic leadership. Earlier, his doctoral studies were supported by a competitive scholarship from Dassault Aviation and Piaggio Aero, underscoring the industrial relevance of his research. He has also secured major research grants from Erasmus+, CNRST Morocco, TUBITAK Turkey, and the OCP Foundation, amounting to several million dirhams. These accolades reflect not only the scientific merit of his work but also its strategic importance in fields such as transportation, defense, AI, and healthcare systems optimization.

Research Skills

Dr. Abou El Majd possesses a robust and diverse skill set spanning numerical optimization, algorithm design, scientific programming, and AI integration. He is proficient in developing multilevel optimization frameworks, parameterization techniques, and model reduction strategies. His capabilities extend to software architecture, cloud platform development, and AI-enhanced decision systems. He has implemented and applied methods like genetic algorithms, particle swarm optimization, and POD-based reduced-order modeling. His technical expertise is complemented by certifications in Business Intelligence (IBM), Big Data Analytics (Griffith University), and Artificial Intelligence (Accenture). His ability to lead multidisciplinary teams and integrate computational techniques with industrial challenges makes him an effective innovator and collaborator. He also brings strong experience in simulation, CFD, robust design, and real-time optimization across diverse technological sectors.

Publication Top Notes

  • Title: Calibration of POD Reduced‐Order Models Using Tikhonov Regularization
    Authors: L. Cordier, B. Abou El Majd, J. Favier
    Year: 2010
    Citations: 183

  • Title: Nested and Self-Adaptive Bézier Parameterizations for Shape Optimization
    Authors: J.A. Désidéri, B. Abou El Majd, A. Janka
    Year: 2007
    Citations: 90

  • Title: A Particle Swarm Optimization Based Algorithm for Primary User Emulation Attack Detection
    Authors: W.F. Fihri, Y. Arjoune, H. El Ghazi, N. Kaabouch, B. Abou El Majd
    Year: 2018
    Citations: 34

  • Title: Multilevel Strategies for Parametric Shape Optimization in Aerodynamics
    Authors: B. Abou El Majd, J.A. Désidéri, R. Duvigneau
    Year: 2008
    Citations: 28

  • Title: Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure
    Authors: Z. El Mrabet, M. Ezzari, H. Elghazi, B.A. El Majd
    Year: 2019
    Citations: 24

  • Title: Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter
    Authors: Z.E. Mrabet, Y. Arjoune, H.E. Ghazi, B.A.A. Majd, N. Kaabouch
    Year: 2018
    Citations: 23

  • Title: Embedding Lattice Structures into Ship Hulls for Structural Optimization and Additive Manufacturing
    Authors: A. Armanfar, A.A. Tasmektepligil, R.T. Kilic, S. Demir, S. Cam, Y. Karafi, B.A. El Majd, et al.
    Year: 2024
    Citations: 20

  • Title: Bayesian Decision Model with Trilateration for Primary User Emulation Attack Localization in Cognitive Radio Networks
    Authors: W.F. Fihri, H. El Ghazi, N. Kaabouch, B.A. El Majd
    Year: 2017
    Citations: 20

  • Title: Homogenization of Periodic Structured Materials with Chiral Properties
    Authors: O. Ouchetto, B.A. El Majd, H. Ouchetto, B. Essakhi, S. Zouhdi
    Year: 2016
    Citations: 20

  • Title: Aerodynamic Shape Optimization Using a Full and Adaptive Multilevel Algorithm
    Authors: B. Abou El Majd, R. Duvigneau, J.A. Désidéri
    Year: 2006
    Citations: 20

  • Title: LSTM-Based Neural Network Architecture for Predicting the Nonlinear Dynamic Behavior of Functional Gradient Viscoelastic Porous Plates
    Year: 2025
    Citations: 1

  • Title: Robust Shape Optimization Using Artificial Neural Networks Based Surrogate Modeling for an Aircraft Wing
    Year: 2024
    Citations: 2

Conclusion

Dr. Badr Abou El Majd exemplifies the modern researcher—analytically rigorous, deeply interdisciplinary, and globally connected. With a rich academic background, impactful research portfolio, and broad leadership experience, he stands as a leading figure in applied mathematics and computational engineering. His ability to bridge theoretical models with industrial and societal needs—through projects in aerospace, healthcare, and smart systems—demonstrates both vision and practical utility. Through international collaborations, patents, high-impact publications, and mentorship roles, he continues to shape emerging research frontiers. His blend of technical depth, strategic leadership, and commitment to innovation make him not only a deserving recipient of high research honors but also a catalyst for future scientific and technological advancements in global contexts.

liang cao | Interdisciplinary Mathematics | Best Researcher Award

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Dr. liang cao | Interdisciplinary Mathematics | Best Researcher Award

lecturer at Hunan Institute of Engineering, China 

Dr. Liang Cao, a faculty member at the Hunan Institute of Engineering, specializes in reliability analysis, wind energy technology, and advanced manufacturing. With a strong academic foundation from Xiangtan University, he has led funded research projects, including one supported by the Natural Science Foundation of Hunan Province. His contributions to structural reliability analysis include developing machine learning-based surrogate models for evaluating low failure probabilities, advancing computational efficiency in engineering. He has published in high-impact journals such as Smart Materials and Structures and Probabilistic Engineering Mechanics and holds multiple patents in mechanical engineering. A member of the Society of Mechanical Engineering, Dr. Cao’s research significantly impacts reliability-based design optimization, particularly in wind turbine gearboxes and robotic mechanisms. While his academic influence is growing, enhancing citation impact, industry collaborations, and editorial leadership could further strengthen his profile. His work continues to shape advancements in probabilistic mechanics and reliability engineering.

Professional Profile 

Scopus Profile
ORCID Profile

Education 

Dr. Liang Cao obtained his academic training from Xiangtan University, where he specialized in mechanical engineering. His education provided a strong foundation in reliability analysis, wind energy technology, and advanced manufacturing. During his academic journey, he gained expertise in probabilistic mechanics, structural safety, and optimization techniques, which later became the focus of his research. His studies emphasized the integration of computational modeling and experimental methods, equipping him with the skills necessary for advancing engineering reliability. Through coursework and research projects, he developed a deep understanding of mechanical system optimization, particularly in developing surrogate models for evaluating failure probabilities. His education laid the groundwork for his career in academia, where he continues to apply theoretical and computational approaches to improve structural and mechanical reliability. With a commitment to academic excellence, Dr. Cao remains engaged in continuous learning and professional development to further enhance his contributions to the field.

Professional Experience 

Dr. Liang Cao serves as a faculty member at the Hunan Institute of Engineering, where he contributes to teaching and research in mechanical engineering. His expertise in reliability analysis and design optimization has enabled him to guide students and researchers in developing innovative solutions for mechanical system reliability. Over the years, he has successfully led projects funded by the Natural Science Foundation of Hunan Province, further solidifying his reputation as an expert in the field. His work integrates computational modeling, machine learning, and structural safety to improve the performance of mechanical systems, particularly in wind turbine gearboxes and robotic mechanisms. Beyond research, he is actively involved in mentoring students and collaborating with peers to advance mechanical engineering methodologies. While he has made significant strides in academia, expanding his industry collaborations and assuming editorial or leadership roles would further strengthen his professional influence and contributions to the field.

Research Interest

Dr. Liang Cao’s research focuses on reliability analysis, probabilistic mechanics, and structural optimization in mechanical engineering. His work integrates machine learning techniques with reliability-based design optimization to improve the efficiency and accuracy of failure predictions. A key aspect of his research is the development of surrogate models, such as Radial Basis Function Neural Networks (RBFNN), for evaluating low failure probabilities with enhanced computational efficiency. His studies have direct applications in wind turbine gearboxes, robotic mechanisms, and piezoelectric dispensing systems, contributing to safer and more robust mechanical designs. Additionally, he explores multi-source uncertainty modeling to enhance structural reliability under variable conditions. His research is published in high-impact journals such as Smart Materials and Structures and Probabilistic Engineering Mechanics. Moving forward, expanding interdisciplinary collaborations and securing larger research grants could amplify the impact of his work on global mechanical engineering challenges.

Awards and Honors 

Dr. Liang Cao has received recognition for his contributions to mechanical engineering, particularly in reliability analysis and probabilistic mechanics. His research achievements have been supported by the Natural Science Foundation of Hunan Province, which funded his work on sliding bearing lubrication reliability in fan gearboxes. Additionally, his multiple patents reflect his innovative contributions to structural safety and optimization in mechanical systems. While he has gained credibility through journal publications in esteemed outlets such as Probabilistic Engineering Mechanics and Smart Materials and Structures, broader recognition through industry awards and professional society honors could further elevate his profile. Active participation in international research collaborations and engineering awards may increase his chances of securing prestigious research awards. By continuing to contribute to mechanical engineering advancements, Dr. Cao has the potential to earn more accolades, further solidifying his standing as a leading researcher in reliability engineering and mechanical system optimization.

Conclusion 

Dr. Liang Cao is an accomplished researcher in mechanical engineering, specializing in reliability analysis, probabilistic mechanics, and structural optimization. With a strong educational foundation from Xiangtan University and professional experience at the Hunan Institute of Engineering, he has made significant contributions to enhancing mechanical system safety and efficiency. His research, funded by the Natural Science Foundation of Hunan Province, has led to innovative developments in surrogate modeling and uncertainty analysis. He has published extensively in high-impact journals and holds multiple patents, reflecting his commitment to advancing engineering methodologies. While his academic impact is commendable, expanding his industry collaborations, citation influence, and leadership roles in research communities could further enhance his professional standing. With a growing reputation in reliability engineering, Dr. Cao is poised to make even greater contributions to mechanical system design and optimization, positioning himself as a leading figure in applied engineering research.

Publications Top Noted

  • Title: Optimizing Dispensing Performance of Needle-Type Piezoelectric Jet Dispensers: A Novel Drive Waveform Approach
    Authors: Liang Cao, S.G. Gong, Y.R. Tao, S.Y. Duan
    Year: 2024
    Source: Smart Materials and Structures

  • Title: Theoretical Study and Physical Tests on the Influence of Process Parameters of Needle on Dispensing Quality
    Authors: Liang Cao, S.G. Gong, S.Y. Duan, Y.R. Tao
    Year: 2023
    Source: Optik

  • Title: A RBFNN Based Active Learning Surrogate Model for Evaluating Low Failure Probability in Reliability Analysis
    Authors: Liang Cao, S.G. Gong, Y.R. Tao, S.Y. Duan
    Year: 2023
    Source: Probabilistic Engineering Mechanics

  • Title: Optimisation Design for Wind Turbine Mainshaft Bearing Based on Lubrication Reliability
    Authors: Liang Cao
    Year: 2020
    Source: International Journal of Reliability and Safety

  • Title: A Novel Evidence-Based Fuzzy Reliability Analysis Method for Structures
    Authors: Liang Cao
    Year: 2017
    Source: Structural and Multidisciplinary Optimization

  • Title: Safety Analysis of Structures with Probability and Evidence Theory
    Authors: Liang Cao
    Year: 2016
    Source: International Journal of Steel Structures