Zhongjun Yan | Energy and Sustainability | Best Researcher Award

Dr. Zhongjun Yan | Energy and Sustainability | Best Researcher Award

Dr. Zhongjun Yan | Energy and Sustainability | Associate Professor | Hunan University of Humanities Science and Technology | China

Dr. Zhongjun Yan is a distinguished scholar and lecturer at the School of Energy and Electrical Engineering, Hunan University of Humanities, Science and Technology. His professional journey reflects an enduring commitment to advancing renewable energy systems, sustainable heating and cooling technologies, and innovative energy storage methods. With strong academic credentials and a growing body of impactful research, Dr. Zhongjun Yan has established himself as an emerging leader in energy engineering. His work bridges theoretical modeling, computational methods, and experimental studies, enabling both scientific innovation and practical applications. He has become a significant contributor to the field of energy storage and solar-based systems, earning recognition through quality publications and participation in national-level and institutional research projects.

Professional Profileย 

Education

Dr. Zhongjun Yan earned his doctoral degree in Heating, Ventilation, and Air-Conditioning Engineering from Hunan University, where his research was focused on the unconstrained melting process of phase change materials and the enhancement of heat transfer in thermal storage systems. His education combined advanced computational modeling, simulation techniques, and experimental validation, shaping a comprehensive expertise in sustainable energy. The doctoral thesis he completed contributed directly to the understanding of thermal storage efficiency and optimization methods in hot water tanks, providing new directions for future energy system development. This academic foundation has been critical to his current research on solar heating, air-conditioning, and the performance of energy storage units.

Experience

In his role as a lecturer, Dr. Zhongjun Yan has undertaken responsibilities that extend across teaching, mentoring, and research. His involvement in the Outstanding Youth Program of the Hunan Provincial Department of Education reflects his growing leadership in energy-related projects. He has contributed to the design and execution of research focusing on computational fluid dynamics (CFD), unconstrained melting phenomena, and heat transfer enhancement. His academic service also includes guiding students in advanced research methodologies and promoting innovation in energy storage and renewable systems. Moreover, Dr. Zhongjun Yan has presented his work at respected international conferences, where he has shared novel findings on the performance of phase change materials. Through these engagements, he has built professional connections that have broadened his collaborative network in the global energy research community.

Research Interest

Dr. Zhongjun Yanโ€™s research interests lie at the intersection of renewable energy systems, thermal engineering, and phase change materials. His primary focus is on the heat transfer performance and optimization of phase change materials to improve the efficiency of thermal energy storage. He has dedicated extensive work to developing new approaches for enhancing the functionality of solar heating water systems and solar air-conditioning systems. Additionally, his research explores innovative modeling techniques to simulate unconstrained melting behaviors, allowing for more accurate predictions of system performance. His long-term vision is to create energy storage and distribution methods that can significantly reduce reliance on non-renewable resources and address the growing global demand for sustainable solutions.

Award

Dr. Zhongjun Yan has been actively involved in award-nominated research programs, most notably recognized through the Outstanding Youth Program by the Hunan Provincial Department of Education. This recognition highlights his innovative contributions to the study of energy storage systems and his leadership potential in advancing renewable technologies. His dedication to pushing the boundaries of engineering solutions in energy efficiency positions him as a strong candidate for international recognition and professional excellence awards.

Selected Publications

  • Performance enhancement of cylindrical latent heat storage units in hot water tanks via wavy design, Renewable Energy, published 2023, 55 citations.

  • A hybrid method for modeling the unconstrained melting of phase change material in hot water tanks, Energy and Buildings, published 2022, 38 citations.

  • Unconstrained melting of phase change material in cylindrical containers inside hot water tanks: Numerical investigation and effect of aspect ratios, Journal of Energy Storage, published 2022, 42 citations.

  • Impact of ultrasound on the melting process and heat transfer of phase change material, presented at International Conference on Applied Energy, published 2018, 27 citations.

Conclusion

Dr. Zhongjun Yan has made meaningful contributions to the field of renewable energy and thermal systems engineering. His work has advanced the knowledge of phase change materials, enhanced the performance of energy storage units, and contributed to the improvement of solar-based heating and cooling technologies. His consistent research output in respected journals, combined with his active role in academic projects and presentations at international conferences, highlights his professional dedication and scientific influence. Dr. Zhongjun Yan is not only a promising researcher but also a mentor and contributor to the broader academic and energy community. With a strong trajectory of impactful research and leadership potential, he represents the qualities of innovation, academic excellence, and societal contribution that make him highly deserving of recognition through this award nomination.

 

Minseok Ryu | Energy and Sustainability | Best Researcher Award

Minseok Ryu | Energy and Sustainability | Best Researcher Award

Assist Prof Dr Minseok Ryu, Arizona State University, United States

Dr. Minseok Ryu is an Assistant Professor at Arizona State Universityโ€™s School of Computing and Augmented Intelligence since August 2023 ๐Ÿ‘จโ€๐Ÿซ. He earned his Ph.D. in Industrial and Operations Engineering from the University of Michigan in 2020 ๐ŸŽ“. His research focuses on optimization and machine learning applications in power systems and privacy-preserving federated learning ๐Ÿ”โšก. Dr. Ryu has held positions at Argonne National Laboratory and Los Alamos National Laboratory ๐Ÿข. He has received numerous awards, including the 2024 Alliance Fellowship at Mayo Clinic and ASU and multiple research highlights from the DOE-ASCR ๐ŸŒŸ.

Publication profile

google scholar

Education

Minseok Ryu holds a Ph.D. in Industrial and Operations Engineering from the University of Michigan, Ann Arbor, which he completed in May 2020 ๐ŸŽ“. Before that, he earned an M.S. in Aerospace Engineering from KAIST, Daejeon, Korea, in February 2014 ๐Ÿš€. His academic journey began with a B.S. in Aerospace Engineering from KAIST, which he obtained in February 2012 โœˆ๏ธ.

Employment

Minseok Ryu is currently an Assistant Professor at the School of Computing and Augmented Intelligence at Arizona State University in Tempe, AZ (Aug 2023โ€“present) ๐Ÿ“š. Previously, he was a Postdoctoral Appointee at the Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL (Aug 2020โ€“Jul 2023) ๐Ÿ”ฌ. He also worked as a Research Assistant with the Applied Mathematics and Plasma Physics Group, Los Alamos National Laboratory, Los Alamos, NM (May 2019โ€“Aug 2019) ๐Ÿงช. Additionally, he served as a Post Baccalaureate Research Fellow at the Kellogg School of Management, Northwestern University, Evanston, IL (Nov 2014โ€“Apr 2015) ๐ŸŽ“.

Honors & Awards

Minseok Ryu has achieved numerous accolades throughout his career. In 2024, he was honored as an Alliance Fellow by the Mayo Clinic and ASU Alliance for Health Care and participated in the Faculty Summer Residency program. His research was highlighted by the Department of Energyโ€™s Advanced Scientific Computing Research (DOE-ASCR) in both 2023 and 2022. Ryu received the Rackham Graduate Student Research Grant in 2016 and multiple fellowships from the University of Michigan in 2015. Additionally, he earned the National Science Foundation Student Award from INFORMS Computing Society. Earlier, he received the National Scholarship from the Korean government (2010-2013) and accolades from KAIST, including the Department Honor and Best Technical Poster Award in 2010. ๐ŸŽ“๐Ÿ”ฌ๐Ÿ“Š

Presentations

Minseok Ryu has made significant contributions to various fields, presenting his research at numerous esteemed conferences. His work includes heuristic algorithms for geomagnetically induced current blocking devices (Paris, June 2024) ๐ŸŒโšก, generating columns (Phoenix, Oct 2023) ๐Ÿ“, and differentially private algorithms for constrained federated learning (Seattle and Amsterdam, 2023) ๐Ÿ”’๐Ÿค–. He has also focused on privacy-preserving federated learning frameworks (Arlington, Aug 2022; Virtual, June 2022) ๐Ÿ›ก๏ธ๐Ÿ“ก. Additionally, he has explored optimal power flow control, transmission expansion planning, and robust optimization in healthcare staffing across various platforms including INFORMS, SIAM, and international symposiums ๐ŸŒ๐Ÿง‘โ€โš•๏ธ

Research focus

Minseok Ryu’s research primarily focuses on data-driven optimization and privacy-preserving techniques, particularly in federated learning and power systems. His work spans several areas, including robust optimization under uncertainty, privacy-preserving distributed control, and federated learning frameworks. Key applications include improving nurse staffing models, optimizing electric grids against geomagnetic disturbances, and developing secure frameworks for federated learning in biomedical research. Ryu’s contributions are significant in ensuring privacy and robustness in distributed systems and optimization problems. ๐Ÿง ๐Ÿ”’๐Ÿ’ก๐Ÿ”‹๐Ÿ‘ฉโ€โš•๏ธ

Publication top notes

Data-Driven Distributionally Robust Appointment Scheduling over Wasserstein Balls

APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning

A Privacy-Preserving Distributed Control of Optimal Power Flow

An extended formulation of the convex recoloring problem on a tree

Nurse Staffing under Absenteeism: A Distributionally Robust Optimization Approach

Differentially private federated learning via inexact ADMM with multiple local updates

Mitigating the Impacts of Uncertain Geomagnetic Disturbances on Electric Grids: A Distributionally Robust Optimization Approach

Algorithms for Mitigating the Effect of Uncertain Geomagnetic Disturbances in Electric Grids

Development of an Engineering Education Framework for Aerodynamic Shape Optimization

Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx