Yang Zhang | Tourism management | Best Researcher Award

Yang Zhang | Tourism management | Best Researcher Award

Assoc. Prof. Dr Yang Zhang, Macau University of Science and Technology, China

Assoc. Prof. Dr. Yang Zhang, a tourism and cultural heritage expert, holds a Ph.D. in cultural heritage conservation from Sichuan University πŸŽ“. As an Associate Editor for the Journal of Leisure Research and editorial board member for multiple journals πŸ“˜, he is deeply involved in academic publishing. His research spans cultural tourism, heritage management, and sustainable practices 🌍, with notable work on Macau’s urban image and sustainable tourism strategies. Dr. Zhang has earned numerous awards, including the Wiley Top Cited Article (2022-2023) πŸ†. He also led impactful projects on public health and tourism resilience 🏨, showcasing his expertise in tourism management.

Publication Profile

Scopus

Academic Qualifications

Assoc. Prof. Dr. Yang Zhang is a dedicated academic with expertise in cultural heritage and tourism. πŸŒπŸ“œ He earned his Ph.D. in Cultural Heritage Conservation and Exploitation from Sichuan University (2007-2010), where he also completed a Master’s in Cultural Tourism (2004-2007) and a Bachelor’s in Tourism Management (2000-2004). πŸ“šπŸŽ“ Dr. Zhang’s extensive academic journey at Sichuan University has fueled his passion for preserving cultural treasures and promoting sustainable tourism. πŸŒΏπŸ›οΈ His work combines academic rigor with a deep commitment to safeguarding cultural assets, making a meaningful impact in both conservation and tourism sectors.

Professional Qualifications and Editorial Roles

Assoc. Prof. Dr. Yang Zhang’s influence extends beyond academia, actively contributing to top journals in tourism, leisure, and hospitality πŸŒπŸ“š. As an Associate Editor for the Journal of Leisure Research and an editorial board member for multiple prominent publications, Dr. Zhang plays a crucial role in shaping knowledge dissemination and upholding academic standards πŸ“ˆβœοΈ. His dedication to advancing research and insights in his field reinforces the academic integrity and impact of these disciplines, making him a respected figure in the scholarly community. Dr. Zhang’s commitment enhances the quality and reach of vital research findings πŸ†βœ¨.

Awards

Associate Professor Dr. Yang Zhang has received numerous prestigious awards in hospitality and tourism research. Recognized as a Wiley Top Cited Author (2022-2023), he has also earned distinctions like the Best Student Presenter at the 10th International Conference on Hospitality and Tourism Management (2024) and third place for Best Research Paper at the World Conference on Smart Tourism (2024). Noteworthy honors include the LIB Academic Research Award (2020/2021), the Outstanding Performance in Online Teaching (2020), and a 10-Year Service Award from Macau University of Science and Technology (2021). His impactful work continues to shape the field. πŸ“šπŸ†πŸŒ

Project Experience

Assoc. Prof. Dr. Yang Zhang has led numerous projects as Principal Investigator, focusing on tourism, culture, and heritage in the Greater Bay Area and beyond. His work includes studying Macau’s urban image through online text analysis with Fundação Macau, examining sustainable tourism at YongDing’s World Heritage Site for PATA, and preparing intangible cultural heritage management strategies for Macau’s Cultural Affairs Bureau. Dr. Zhang also researched hotel industry strategies for public health emergencies and film-induced tourism in the Greater Bay Area. Recently, he has focused on regional tourism integration and culinary tourism, supporting Greater Bay Area tourism growth πŸŒπŸ¨πŸŽ¬πŸ²πŸ“ˆ.

Publication Top Notes

From disruption to normalcy: Co-production public service and destination competitiveness

Dr. Zhang’s extensive research background, editorial influence, impactful publications, awards, and project leadership, he is an exemplary candidate for the Best Researcher Award. His work significantly advances tourism, hospitality, and cultural heritage management, making him a deserving recipient.Β 

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 University’s School of Computing and Augmented Intelligence since August 2023 πŸ‘¨β€πŸ«. He earned his Ph.D. in Industrial and Operations Engineering from the University of Michigan in 2020 πŸŽ“. His research focuses on optimization and machine learning applications in power systems and privacy-preserving federated learning πŸ”βš‘. Dr. Ryu has held positions at Argonne National Laboratory and Los Alamos National Laboratory 🏒. He has received numerous awards, including the 2024 Alliance Fellowship at Mayo Clinic and ASU and multiple research highlights from the DOE-ASCR 🌟.

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 πŸŽ“. Before that, he earned an M.S. in Aerospace Engineering from KAIST, Daejeon, Korea, in February 2014 πŸš€. His academic journey began with a B.S. in Aerospace Engineering from KAIST, which he obtained in February 2012 ✈️.

Employment

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

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 Energy’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. πŸŽ“πŸ”¬πŸ“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) 🌍⚑, generating columns (Phoenix, Oct 2023) πŸ“, and differentially private algorithms for constrained federated learning (Seattle and Amsterdam, 2023) πŸ”’πŸ€–. He has also focused on privacy-preserving federated learning frameworks (Arlington, Aug 2022; Virtual, June 2022) πŸ›‘οΈπŸ“‘. 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 πŸŒπŸ§‘β€βš•οΈ

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. πŸ§ πŸ”’πŸ’‘πŸ”‹πŸ‘©β€βš•οΈ

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