Shijie Zhao | Applied Mathematics | Best Researcher Award

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Assoc. Prof. Dr. Shijie Zhao | Applied Mathematics | Best Researcher Award

Associate Professor at Liaoning Technical University, China

Assoc. Prof. Dr. Shijie Zhao is a distinguished researcher and academic at the Institute of Intelligence Science and Optimization, Liaoning Technical University, China. With a Ph.D. in Optimization and Management Decisions, his expertise lies in metaheuristic optimization, multi-objective optimization, and underwater navigation and positioning. He has made significant contributions through innovative algorithm designs and novel mathematical models, particularly in high-dimensional feature selection and robust navigation techniques. Dr. Zhao has published 9 SCI-indexed journal articles and participated in over 10 nationally and provincially funded research projects. He serves as a reviewer for leading journals including those by Elsevier, Springer, and IEEE, and holds memberships in 13 professional bodies. With strong programming skills, rigorous analytical thinking, and a commitment to scientific innovation, Dr. Zhao has also earned four research awards. His work bridges theoretical mathematics and practical applications, making him a valuable contributor to the global research community in intelligent systems and optimization.

Professional Profile 

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Education

Assoc. Prof. Dr. Shijie Zhao has a robust academic foundation anchored at Liaoning Technical University, China. He earned his B.S. degree in Science of Information & Computation in 2012, followed by a successive postgraduate and doctoral program in Mathematics and Applied Mathematics from 2012 to 2014. He went on to complete his Ph.D. in Optimization and Management Decisions in 2018. His educational trajectory highlights a deep commitment to the field of mathematical optimization and intelligent systems. Dr. Zhao’s academic excellence is also reflected in his ability to integrate theoretical knowledge with practical problem-solving, laying a strong foundation for his future research. His interdisciplinary approach blends pure mathematics with applied optimization techniques, making him uniquely positioned to contribute to emerging challenges in computational intelligence, machine learning, and navigation systems. His comprehensive training has equipped him with skills in advanced mathematical modeling, algorithm design, and statistical analysis—all crucial for his research trajectory.

Professional Experience

Dr. Shijie Zhao began his professional journey as a faculty member at Liaoning Technical University, where he is now serving as an Associate Professor and Director of the Institute of Intelligence Science and Optimization. Since 2012, he has progressed through a series of academic roles, including a postdoctoral tenure beginning in 2020. He has successfully led and participated in a range of scientific research projects sponsored by institutions such as the China Postdoctoral Science Foundation and the Department of Science & Technology of Liaoning Province. In addition to his teaching responsibilities, he has been actively involved in administrative, academic, and research leadership roles. Dr. Zhao has served as a reviewer for numerous high-impact international journals and conferences and has editorial roles in reputed scientific publications. His contributions to collaborative and interdisciplinary projects underscore his ability to bridge research and real-world applications, enhancing his standing as a key contributor in intelligent systems research.

Research Interest

Assoc. Prof. Dr. Shijie Zhao’s research interests lie at the intersection of intelligent optimization, computational mathematics, and advanced data analytics. He specializes in the development and enhancement of metaheuristic and multi-objective optimization algorithms, addressing both theoretical and application-driven challenges. His work has pioneered novel strategies for high-dimensional feature selection and optimization in machine learning contexts. Another key area of his focus is underwater navigation and positioning, where he has introduced innovative models for enhancing gravity navigation accuracy. With a strong foundation in mathematics, Dr. Zhao combines theoretical rigor with practical applicability, ensuring that his research contributes both to academic knowledge and technological development. His recent work explores how optimization strategies can be integrated into real-time systems, with implications in robotics, autonomous navigation, and engineering design. By addressing complex computational problems, Dr. Zhao’s research plays a vital role in driving forward the capabilities of intelligent systems and adaptive algorithms.

Award and Honor

Dr. Shijie Zhao has earned multiple accolades in recognition of his impactful contributions to scientific research and innovation. He has received four prestigious research awards for his work in intelligent systems, mathematical optimization, and applied computational modeling. His leadership in various national and provincial research initiatives has further cemented his reputation as a top-tier researcher in his domain. In addition to these honors, he has held editorial and reviewer positions for over ten internationally recognized journals, including publications by IEEE, Springer, and Elsevier—an acknowledgment of his expertise and academic integrity. Dr. Zhao is also an active member of 13 professional bodies, reflecting his global engagement and scholarly influence. His participation in high-impact collaborative projects and his growing citation index underscore the recognition and respect he commands in the research community. These honors validate his innovative spirit and unwavering dedication to advancing knowledge in mathematics and intelligent computing.

Conclusion

In conclusion, Assoc. Prof. Dr. Shijie Zhao exemplifies excellence in mathematical research, optimization theory, and intelligent system applications. His educational background, combined with over a decade of professional experience, positions him as a thought leader in his field. Through pioneering contributions to metaheuristic algorithms, multi-objective optimization, and underwater navigation, he bridges the gap between theoretical frameworks and practical technologies. His commitment to research integrity, academic service, and innovation has earned him widespread recognition and professional accolades. As an educator, leader, and scientist, Dr. Zhao’s multifaceted contributions reflect a deep dedication to advancing scientific knowledge and solving complex global challenges. His future endeavors are poised to have even greater impacts on the fields of artificial intelligence, data-driven decision-making, and intelligent navigation. With a strong publication record, a solid foundation in mathematics, and an expanding research network, Dr. Zhao continues to be a prominent and influential figure in the global academic landscape.

Publications Top Notes

  • Title: ID2TM: A Novel Iterative Double-Cross Domain-Center Transfer-Matching Method for Underwater Gravity-Aided Navigation
    Authors: Shijie Zhao, Zhiyuan Dou, Huizhong Zhu, Wei Zheng, Yifan Shen
    Year: 2025
    Source: IEEE Internet of Things Journal

  • Title: OS-BiTP: Objective sorting-informed bidomain-information transfer prediction for dynamic multiobjective optimization
    Authors: Shijie Zhao, Tianran Zhang, Lei Zhang, Jinling Song
    Year: 2025
    Source: Swarm and Evolutionary Computation

  • Title: Mirage search optimization: Application to path planning and engineering design problems
    Authors: Jiahao He, Shijie Zhao, Jiayi Ding, Yiming Wang
    Year: 2025
    Source: Advances in Engineering Software

  • Title: Twin-population Multiple Knowledge-guided Transfer Prediction Framework for Evolutionary Dynamic Multi-Objective Optimization
    Authors: Shijie Zhao, Tianran Zhang, Miao Chen, Lei Zhang
    Year: 2025
    Source: Applied Soft Computing

  • Title: VC-TpMO: V-dominance and staged dynamic collaboration mechanism based on two-population for multi- and many-objective optimization algorithm
    Authors: Shijie Zhao, Shilin Ma, Tianran Zhang, Miao Chen
    Year: 2025
    Source: Expert Systems with Applications

  • Title: A Novel Cross-Line Adaptive Domain Matching Algorithm for Underwater Gravity Aided Navigation
    Authors: Shijie Zhao, Wei Zheng, Zhaowei Li, Huizhong Zhu, Aigong Xu
    Year: 2024
    Source: IEEE Geoscience and Remote Sensing Letters

  • Title: Triangulation topology aggregation optimizer: A novel mathematics-based meta-heuristic algorithm for continuous optimization and engineering applications
    Authors: Shijie Zhao, Tianran Zhang, Liang Cai, Ronghua Yang
    Year: 2024
    Source: Expert Systems with Applications

  • Title: Improving Matching Efficiency and Out-of-Domain Positioning Reliability of Underwater Gravity Matching Navigation Based on a Novel Domain-Center Adaptive-Transfer Matching Method
    Authors: Shijie Zhao, Wei Zheng, Zhaowei Li, Huizhong Zhu, Aigong Xu
    Year: 2023
    Source: IEEE Transactions on Instrumentation and Measurement

  • Title: A dynamic support ratio of selected feature-based information for feature selection
    Authors: Shijie Zhao, Mengchen Wang, Shilin Ma, Qianqian Cui
    Year: 2023
    Source: Engineering Applications of Artificial Intelligence

  • Title: Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems
    Authors: Shijie Zhao, Tianran Zhang, Shilin Ma, Mengchen Wang
    Year: 2023
    Source: Applied Intelligence

  • Title: Improving the Out-of-Domain Matching Reliability and Positioning Accuracy of Underwater Gravity Matching Navigation Based on a Novel Cyclic Boundary Semisquare-Domain Researching Method
    Authors: Shijie Zhao, Wei Zheng, Zhaowei Li, Huizhong Zhu, Aigong Xu
    Year: 2023
    Source: IEEE Sensors Journal

  • Title: A feature selection method via relevant-redundant weight
    Authors: Shijie Zhao, Mengchen Wang, Shilin Ma, Qianqian Cui
    Year: 2022
    Source: Expert Systems with Applications

  • Title: Dandelion Optimizer: A nature-inspired metaheuristic algorithm for engineering applications
    Authors: Shijie Zhao, Tianran Zhang, Shilin Ma, Miao Chen
    Year: 2022
    Source: Engineering Applications of Artificial Intelligence

  • Title: Improving Matching Efficiency and Out-of-domain Reliability of Underwater Gravity Matching Navigation Based on a Novel Soft-margin Local Semicircular-domain Re-searching Model
    Authors: Shijie Zhao, Wei Zheng, Zhaowei Li, Huizhong Zhu, Aigong Xu
    Year: 2022
    Source: Remote Sensing

  • Title: Improving Matching Accuracy of Underwater Gravity Matching Navigation Based on Iterative Optimal Annulus Point Method with a Novel Grid Topology
    Authors: Shijie Zhao, Wei Zheng, Zhaowei Li, Aigong Xu, Huizhong Zhu
    Year: 2021
    Source: Remote Sensing

  • Title: A Novel Quantum Entanglement‐Inspired Meta‐heuristic Framework for Solving Multimodal Optimization Problems
    Authors: Shijie Zhao
    Year: 2021
    Source: Chinese Journal of Electronics

  • Title: A Novel Modified Tree‐Seed Algorithm for High‐Dimensional Optimization Problems
    Authors: Shijie Zhao
    Year: 2020
    Source: Chinese Journal of Electronics

 

Halima Bensmail | Applied Mathematics | Best Researcher Award

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Prof. Dr. Halima Bensmail | Applied Mathematics | Best Researcher Award

Principal scientist at Qatar Computing Research Institute, Qatar

Dr. Halima Bensmail is a distinguished Principal Scientist at the Qatar Computing Research Institute, specializing in machine learning, bioinformatics, biostatistics, and statistical modeling. With a Ph.D. in Statistics (Summa Cum Laude) from the University Pierre & Marie Curie, she has made significant contributions to Bayesian inference, multivariate analysis, and precision medicine. She has an impressive research record with an H-index of 31, i10-index of 54, and around 140 publications in prestigious journals such as Nature Communications, JASA, and IEEE TNNLS. As the founder of the Statistical Machine Learning and Bioinformatics group at QCRI, she has led groundbreaking projects, including the development of open-source data-driven tools like the PRISQ pre-diabetes screening model and MCLUST clustering algorithm. With extensive academic experience in the USA, France, and the Netherlands, she has mentored numerous postdocs and students, shaping the next generation of researchers. Her expertise and leadership make her a key figure in data science and precision health.

Professional Profile 

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Education

Dr. Halima Bensmail holds a Ph.D. in Statistical Machine Learning (Summa Cum Laude) from the University Pierre & Marie Curie (Paris 6), where she specialized in Bayesian inference, spectral decomposition, and mixture models. Her thesis focused on deterministic and Bayesian model-based clustering and classification for data science applications. Prior to that, she earned an M.S. in Machine Learning from the same university, with a focus on probability, financial modeling, and stochastic processes. She also holds a Bachelor’s degree in Applied Mathematics and Statistics from the University Mohammed V in Morocco, where she gained expertise in numerical analysis, stochastic processes, topology, and mathematical programming. Throughout her academic journey, she was mentored by esteemed professors and developed a strong foundation in theoretical and applied statistics. Her educational background has laid the groundwork for her pioneering research in machine learning, bioinformatics, and data-driven modeling for real-world applications.

Professional Experience

Dr. Bensmail is currently a Principal Scientist at the Qatar Computing Research Institute (QCRI), where she leads research in bioinformatics, statistical machine learning, and artificial intelligence. She also serves as a Full Professor in the College of Science and Engineering at Hamad Bin Khalifa University and a Visiting Full Professor at Texas A&M University at Qatar. Previously, she held tenured faculty positions at Virginia Medical School and the University of Tennessee, where she contributed significantly to public health and business administration research. She has also worked as a Research Scientist at the University of Leiden, a scientist at the Fred Hutchinson Cancer Research Center, and a postdoctoral researcher at the University of Washington. With decades of experience across academia and research institutions in the U.S., Europe, and the Middle East, she has built expertise in developing statistical and AI-driven solutions for biomedical and computational challenges.

Research Interests

Dr. Bensmail’s research spans statistical machine learning, bioinformatics, and precision medicine. She has developed novel clustering algorithms, such as an advanced Bayesian clustering model implemented in the MCLUST package, and statistical methods for analyzing Next-Generation Sequencing (NGS) data. She is also interested in computational biology, specifically protein-protein interactions, protein solubility, and structural biology. Her work includes dimensionality reduction techniques like nonnegative matrix factorization and discriminative sparse coding for domain adaptation. In the field of precision medicine, she has designed PRISQ, a statistical model for pre-diabetes screening. Her broader interests include Bayesian statistics, functional data analysis, information theory, and high-dimensional data modeling. With a strong focus on developing real-world data-driven tools, she actively contributes to statistical methodologies that enhance decision-making in medicine, genomics, and artificial intelligence applications.

Awards and Honors

Dr. Bensmail has received numerous accolades for her contributions to machine learning, bioinformatics, and statistical modeling. Her work has been widely recognized, with over 140 peer-reviewed publications and an H-index of 31, demonstrating the impact of her research. She has secured research grants and led major projects in AI-driven healthcare solutions. Her contributions to the field have been acknowledged through invitations to serve as a keynote speaker at international awards and as an editorial board member for high-impact journals. She has also been instrumental in mentoring young researchers, postdoctoral fellows, and doctoral students, fostering the next generation of scientists in AI, statistics, and bioinformatics. Additionally, her work on statistical methods for precision medicine and biomedical informatics has gained international recognition, positioning her as a leading expert in the field of data science for healthcare and computational biology.

Conclusion

Dr. Halima Bensmail is a pioneering researcher in machine learning, statistical modeling, and bioinformatics, with a career spanning leading institutions in the U.S., Europe, and the Middle East. Her contributions to clustering algorithms, high-dimensional data analysis, and precision medicine have made a lasting impact on the fields of AI and computational biology. As a mentor and leader, she has shaped numerous young scientists and postdocs, driving innovation in data science applications. With a robust publication record, influential research projects, and a dedication to developing real-world AI-driven solutions, she stands as a leading figure in statistical machine learning. Her expertise and contributions continue to push the boundaries of knowledge in bioinformatics, artificial intelligence, and healthcare analytics, making her a strong candidate for prestigious research awards and recognition in scientific communities worldwide.

Publications Top Noted

 

Ran Zhang | Applied Mathematics | Best Researcher Award

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Dr. Ran Zhang | Applied Mathematics | Best Researcher Award

Researcher at Nanjing University of Posts and Telecommunications, China

Ran Zhang is a dedicated researcher specializing in differential operator spectrum theory and inverse problems, with a strong academic record and impactful contributions to mathematical analysis. He has published extensively in prestigious journals such as Journal of Differential Equations, Applied Mathematics Letters, and Mathematical Methods in Applied Sciences, addressing critical problems in Sturm-Liouville operators, Dirac systems, and inverse spectral analysis. As the host of national research projects, including those funded by the National Natural Science Foundation of China and Jiangsu Provincial Natural Science Foundation of China, he has demonstrated leadership in advancing theoretical mathematics. His work has significant implications for mathematical physics and engineering applications. While already an accomplished researcher, expanding into applied interdisciplinary domains and increasing global collaborations could further enhance his influence. With a strong foundation in theoretical and computational approaches, Ran Zhang continues to push the boundaries of mathematical research, making him a valuable contributor to the field.

Professional Profile

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Education

Ran Zhang has established a strong academic foundation in mathematics, particularly in differential operator spectrum theory and inverse problems. His educational journey has been marked by rigorous training in advanced mathematical techniques, equipping him with the analytical and computational skills necessary for solving complex problems in spectral analysis. Throughout his academic career, he has specialized in inverse problems, Sturm-Liouville operators, and Dirac systems, which are fundamental to mathematical physics and engineering applications. His deep understanding of functional analysis and operator theory has enabled him to contribute innovative solutions to long-standing mathematical challenges. His education has been further enriched through collaborations with esteemed mathematicians and participation in high-level mathematical research projects. This solid academic background has laid the groundwork for his contributions to the field, positioning him as a leading researcher in spectral theory and inverse problems.

Professional Experience

Ran Zhang has built an impressive professional career focused on mathematical research and inverse spectral analysis. As a host of research projects funded by the National Natural Science Foundation of China and the Jiangsu Provincial Natural Science Foundation of China, he has played a pivotal role in advancing theoretical mathematics. His work has been recognized in esteemed mathematical journals, reflecting the high impact of his research in spectral theory, Sturm-Liouville operators, and discontinuous differential equations. He has actively contributed to solving complex mathematical challenges and has worked closely with research teams, collaborating with renowned mathematicians across institutions. His experience extends beyond academia, as his research has potential applications in engineering, quantum mechanics, and applied physics. His ability to bridge theoretical mathematics with practical applications makes him a distinguished figure in the field. As he progresses in his career, expanding into interdisciplinary research and mentoring young mathematicians could further solidify his professional legacy.

Research Interest

Ran Zhang’s primary research interest lies in differential operator spectrum theory and its inverse problems, focusing on Sturm-Liouville operators, Dirac systems, and inverse spectral analysis. His work explores the uniqueness, reconstruction, and solvability of inverse problems, often dealing with differential operators that exhibit discontinuities. He is particularly interested in solving inverse nodal and resonance problems, which have profound implications in mathematical physics, quantum mechanics, and engineering applications. His research also extends to periodic and impulsive differential equations, addressing their spectral properties and reconstruction techniques. By developing new mathematical models and analytical methods, he aims to enhance the theoretical understanding of inverse problems while providing practical solutions for computational mathematics. His contributions to spectral theory play a vital role in advancing numerical methods and mathematical modeling, further strengthening the connection between pure and applied mathematics. His future research aims to expand into multidisciplinary applications, fostering collaborations across physics, engineering, and computational sciences.

Awards and Honors

Ran Zhang’s research excellence has been recognized through several prestigious honors and awards. As the recipient of funding from the National Natural Science Foundation of China and the Jiangsu Provincial Natural Science Foundation of China, he has demonstrated his ability to lead impactful research projects. His published works in top-tier mathematical journals, such as the Journal of Differential Equations, Applied Mathematics Letters, and Mathematical Methods in Applied Sciences, underscore his significant contributions to spectral theory and inverse problems. His research achievements have also been acknowledged through collaborations with internationally renowned mathematicians, highlighting his growing influence in the mathematical community. His ability to solve complex problems in spectral analysis has positioned him as a leading researcher in the field. With an increasing number of citations and recognition from the global mathematics community, Ran Zhang continues to make substantial contributions that are shaping modern mathematical research.

Conclusion

Ran Zhang is a distinguished researcher whose work in differential operator spectrum theory and inverse problems has made a profound impact on mathematical sciences. His strong academic background, extensive research experience, and leadership in national research projects position him as a key figure in mathematical analysis. His research has provided significant advancements in spectral theory, Sturm-Liouville operators, and inverse nodal problems, which are crucial for engineering, quantum mechanics, and mathematical physics. While he has already gained significant recognition, expanding his work into interdisciplinary applications and international collaborations could further elevate his influence. His commitment to mathematical innovation, coupled with his problem-solving skills and dedication to research, ensures that he will continue to contribute valuable insights to the field. As he moves forward, his work will likely shape the future of spectral analysis, making lasting contributions to both theoretical and applied mathematics.

Publications Top Noted

  • Title: Inverse spectral problems for the Dirac operator with complex-valued weight and discontinuity
    Authors: Ran Zhang, Chuan-Fu Yang, Natalia P. Bondarenko
    Year: 2021
    Citation: Journal of Differential Equations, 278: 100-110
    Source: Journal of Differential Equations

  • Title: Uniqueness and reconstruction of the periodic Strum-Liouville operator with a finite number of discontinuities
    Authors: Ran Zhang, Kai Wang, Chuan-Fu Yang
    Year: 2024
    Citation: Applied Mathematics Letters, 147: 108853
    Source: Applied Mathematics Letters

  • Title: Uniqueness theorems for the impulsive Dirac operator with discontinuity
    Authors: Ran Zhang, Chuan-Fu Yang
    Year: 2022
    Citation: Analysis and Mathematical Physics, 12(1): 1-16
    Source: Analysis and Mathematical Physics

  • Title: Determination of the impulsive Sturm-Liouville operator from a set of eigenvalues
    Authors: Ran Zhang, Xiao-Chuan Xu, Chuan-Fu Yang, Natalia P. Bondarenko
    Year: 2020
    Citation: Journal of Inverse and Ill-posed Problems, 28(3): 341-348
    Source: Journal of Inverse and Ill-posed Problems

  • Title: Solving the inverse problems for discontinuous periodic Strum-Liouville operator by the method of rotation
    Authors: Ran Zhang, Kai Wang, Chuan-Fu Yang
    Year: 2024
    Citation: Results in Mathematics, 79(1): 49
    Source: Results in Mathematics

  • Title: Ambarzumyan-type theorem for the impulsive Sturm-Liouville operator
    Authors: Ran Zhang, Chuan-Fu Yang
    Year: 2021
    Citation: Journal of Inverse and Ill-posed Problems, 29(1): 21-25
    Source: Journal of Inverse and Ill-posed Problems

  • Title: Solvability of an inverse problem for discontinuous Sturm-Liouville operators
    Authors: Ran Zhang, Natalia P. Bondarenko, Chuan-Fu Yang
    Year: 2021
    Citation: Mathematical Methods in Applied Sciences, 44(1): 124-139
    Source: Mathematical Methods in Applied Sciences

  • Title: Reconstruction of the Strum-Liouville operator with periodic boundary conditions and discontinuity
    Authors: Ran Zhang, Chuan-Fu Yang
    Year: 2022
    Citation: Mathematical Methods in Applied Sciences, 45(8): 4244-4251
    Source: Mathematical Methods in Applied Sciences

  • Title: Determination of the impulsive Dirac systems from a set of eigenvalues
    Authors: Ran Zhang, Chuan-Fu Yang, Kai Wang
    Year: 2023
    Citation: Mathematics, 11(19): 4086
    Source: Mathematics

  • Title: Inverse nodal problem for the Sturm-Liouville operator with a weight
    Authors: Ran Zhang, Murat Sat, Chuan-Fu Yang
    Year: 2020
    Citation: Applied Mathematics – A Journal of Chinese Universities Series B, 35(2): 193-202
    Source: Applied Mathematics – A Journal of Chinese Universities Series B