Summary

Dr. Zari Farhadi is a dedicated lecturer and researcher with a Ph.D. in Data Science, specializing in machine learning, predictive modeling, and deep learning. She has developed innovative hybrid models such as ADeFS, RARTEN, ERDeR, and ECAPRAF, contributing to advanced data analysis. Her work includes impactful publications in esteemed journals and a commitment to interdisciplinary research.

Education

• Ph.D. in Data Science
• Academic focus on statistical learning and ensemble methods

Professional Experience

• Lecturer, University of Tabriz, Iran
  Focused on teaching and research in data science and artificial intelligence
• Reviewer for leading journals: PLOS ONE, IEEE Access, Frontiers, and Ain Shams Engineering

Research Interests

• Machine Learning and Deep Learning
• Predictive Modeling and Ensemble Learning
• Development of hybrid models for advanced data analysis
• Statistical Learning and innovative applications in data science

Summary

Dr. Youjie Li is a distinguished academic at Kunming University of Science and Technology, renowned for his contributions to finance and science. With decades of dedication to teaching, research, and editorial excellence, he has significantly influenced his field.

Education

Dr. Li earned his doctorate from the prestigious Southwestern University of Finance and Economics, laying a strong foundation for his illustrious academic journey.

Professional Experience

Dr. Li has devoted his career to Kunming University of Science and Technology, where he has excelled as a professor and mentor. He has also served as an editor for various SCI-indexed journals, showcasing his expertise in research dissemination.

Research Interests

His research focuses on finance, economics, and interdisciplinary studies, reflected in numerous SCI-indexed publications that advance knowledge and innovation.

Early Academic Pursuits

Ms. Hyun Ju Kim’s academic journey began with a strong foundation in marine engineering, earning her Bachelor’s degree from the Korea Maritime and Ocean University in 2017. She further honed her expertise by pursuing a Master’s in Industrial and Data Engineering at Pukyong National University, completing it in 2023. Now a Ph.D. student in the same department, Ms. Kim continues to explore the intersections of industrial engineering and data science, applying her research to the maritime sector.

Professional Endeavors

Ms. Kim has made significant strides in both academia and industry. Starting her professional career at the Korea Marine Equipment Research Institute (KOMERI) as a Senior Researcher, she contributed to numerous projects, including the development of integrated power management systems for hybrid vessels. In December 2024, she transitioned to the Busan Institute of Science and Technology Promotion, where she continues her research. These roles have shaped her into a leader in ship engine and navigation data analysis.

Contributions and Research Focus

Ms. Kim’s research is rooted in data-driven approaches to improving maritime energy efficiency and reducing carbon emissions. As a principal investigator, she developed an integrated power management system for hybrid vessels. Her work extends into predictive modeling, fuel consumption analysis, and environmental impact reduction in the maritime industry. Notably, her journal publications cover topics like fuel-switching behavior in dual-fuel vessels, the prediction of bulk carrier fuel consumption, and the use of explainable AI in energy efficiency optimization.

Impact and Influence

Through her innovative use of AI and machine learning, Ms. Kim has had a significant impact on both the academic and industrial sectors. Her research on fuel consumption prediction models and carbon emission reduction has influenced the maritime industry’s approach to sustainability. Furthermore, her work has been recognized with a Best Paper Award at the ICIoTC 2024 conference, solidifying her reputation as an emerging expert in the field.

Academic Citations

Ms. Kim’s scholarly contributions are steadily gaining recognition in the academic community, particularly for her first-author journal articles. Her work, such as “A Data-Driven Approach to Analyzing Fuel-Switching Behavior” and “Development of a Carbon Emission Prediction Model for Bulk Carrier,” has been published in prominent journals like the Journal of Marine Science and Engineering and the Journal of the Korean Institute of Industrial Engineers. These publications highlight her leadership in integrating AI with maritime technologies to solve pressing environmental challenges.

Technical Skills 

Ms. Kim’s technical skills span across several domains, including data analysis, machine learning, and AI. She is proficient in developing predictive models using machine learning algorithms, particularly in the context of maritime fuel consumption and carbon emissions. Her expertise also extends to using Explainable AI (XAI) methodologies, which ensure transparency and interpretability in the results of her research, making her contributions highly applicable to real-world maritime applications.

Teaching Experience

Although Ms. Kim’s primary focus has been on research, her academic background and professional experience have positioned her to share her knowledge with the next generation of engineers. Through her roles at Pukyong National University and as a mentor to fellow researchers, she has contributed to the academic development of students in industrial and data engineering, especially those focusing on maritime applications of AI and data science.

Legacy and Future Contributions

As Ms. Kim continues her Ph.D. studies, her legacy is poised to shape the future of sustainable maritime practices. Her work in developing carbon emission prediction models and optimizing fuel consumption in vessels is crucial for the maritime industry’s transition to more environmentally friendly technologies. With the integration of AI, machine learning, and data analytics into her projects, she is positioned to make groundbreaking contributions to both academia and industry in the coming years.

Early Academic Pursuits

Dr. Bader Alsharif began his academic journey with a Bachelor of Science in Computer Engineering from the College of Technology in Riyadh, Saudi Arabia, where he graduated in 2008. He further advanced his knowledge with a Master of Science in Computer Engineering from the Florida Institute of Technology in 2017. Currently, he is pursuing a Doctorate in Computer Engineering at Florida Atlantic University, with an expected graduation in May 2025. This strong academic foundation has equipped him with a profound understanding of advanced computing principles and methodologies.

Professional Endeavors

Dr. Alsharif has contributed significantly to academia and technology for over a decade. As a lecturer at the Communications and Information College in Riyadh, he led pioneering projects, including establishing Saudi Arabia’s first CISCO simulation lab. At Florida Atlantic University, he currently serves as a Teaching Assistant, mentoring students and fostering innovation through hands-on learning experiences.

Contributions and Research Focus

Dr. Alsharif’s research portfolio spans Artificial Intelligence (AI), Cybersecurity, and the Internet of Things (IoT). His work emphasizes integrating these technologies to enhance healthcare systems, focusing on improving patient outcomes and addressing critical challenges in digital health. With over seven peer-reviewed publications, his research is recognized for its innovation and real-world applications.

Impact and Influence

Dr. Alsharif has demonstrated an unwavering commitment to empowering individuals with special needs. He has designed and implemented curricula tailored to their unique requirements, directly impacting the academic success of over 3,000 students. His efforts to showcase their talents during Arab Deaf Week have brought international attention to inclusivity and accessibility in education.

Academic Citations and Legacy

Dr. Alsharif’s scholarly contributions have been widely cited in fields intersecting AI and Cybersecurity. His dedication to bridging the gap between research and practical applications ensures his legacy as a thought leader in technology-driven healthcare innovation.

Technical Expertise

Proficient in programming languages like C, Python, and MATLAB, Dr. Alsharif possesses advanced skills in GitHub, MS Office, and CISCO technologies. As a Certified Trainer of Trainers and CCNA1 expert, he excels in both technical implementation and knowledge transfer.

Teaching Excellence

With extensive teaching experience, Dr. Alsharif has guided over 3,000 students, including those with special needs, achieving exemplary performance ratings. His role at Florida Atlantic University involves mentoring engineering design projects and addressing technical queries, fostering a supportive learning environment.

Legacy and Future Contributions

Dr. Alsharif envisions leveraging his expertise in AI, IoT, and Cybersecurity to drive impactful innovations in healthcare. His ongoing doctoral research aims to create scalable, technology-driven solutions that revolutionize patient care and accessibility. By integrating technical excellence with a commitment to inclusivity, Dr. Alsharif is poised to make transformative contributions to the fields of education, research, and healthcare technology.

Early Academic Pursuits

Prof. Ying Wang began her academic journey with a Bachelor’s degree in Chemistry from Sichuan University, Chengdu, China (2009–2013). Under the guidance of Prof. Ying Xue (H-index: 33), she demonstrated exceptional talent, earning the “Outstanding Dissertation of Sichuan University” award. She furthered her education with a Doctoral degree at the State Key Laboratory of Precision Spectroscopy, East China Normal University, Shanghai (2013–2018), where she was mentored by esteemed researchers Prof. John Zeng Hui Zhang (H-index: 58), Prof. Xiao He (H-index: 40), and co-advised by Prof. Donald G. Truhlar (H-index: 194). Her dedication earned her the “Shanghai Outstanding Doctoral Graduates” award and the prestigious “President Scholarship of East China Normal University.”

Professional Endeavors

Prof. Ying Wang’s professional career began at Hunan Normal University, where she initially served as an Associate Professor (2018–2020) before being promoted to Professor and Doctoral Supervisor in 2020. She is affiliated with the National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences. Her professional trajectory highlights her commitment to advancing the fields of quantum chemistry and drug development.

Contributions and Research Focus

Prof. Wang specializes in density functional theory, quantum chemistry calculations, and computer-aided drug design. Her research extends to understanding protein-protein, protein-peptide, and protein-ligand interactions. Notably, her work integrates artificial intelligence to revolutionize drug discovery, focusing on developing high-precision predictive models for protein-peptide interaction energies.

Impact and Influence

Prof. Wang has made significant contributions to computational chemistry, evident in her extensive list of 18 high-impact publications. Her groundbreaking work on the M06-SX screened-exchange density functional and the development of novel inhibitors targeting critical biological pathways has garnered international recognition. Her research not only advances theoretical frameworks but also holds practical implications for drug discovery and molecular modeling.

Academic Cites

Prof. Wang’s publications have been featured in esteemed journals such as Nature Computational Science, Proceedings of the National Academy of Sciences USA, and Journal of Chemical Theory and Computation. Her collaborations with leading scientists like Prof. Donald G. Truhlar underscore the global impact of her research.

Technical Skills

Prof. Wang is proficient in advanced quantum chemistry software and computational tools, enabling her to model complex molecular systems with precision. Her expertise extends to artificial intelligence applications in drug discovery, molecular dynamics simulations, and high-throughput screening.

Teaching Experience

As an educator, Prof. Wang is committed to shaping the next generation of scientists. She teaches courses such as Biometrics for undergraduate students and Engineering Mathematics, Molecular Modelling, and Life Science Frontiers for postgraduate students. Her innovative teaching methods inspire students to excel in both theoretical and applied sciences.

Legacy and Future Contributions

Prof. Wang’s contributions to computational chemistry and drug development have laid a strong foundation for future advancements in the field. Her ongoing projects, supported by prestigious grants such as the National Natural Science Foundation of China and the Hunan Provincial Department of Education, promise to push the boundaries of quantum chemistry and artificial intelligence applications. Dr. Wang’s vision for the future emphasizes interdisciplinary collaboration and the development of cutting-edge tools for molecular modeling and drug discovery.