Liang Gao | Percolation on Multilayer Networks | Excellence in Academic Research Award

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Assoc. Prof. Dr. Liang Gao | Percolation on Multilayer Networks | Excellence in Academic Research Award

Liang Gao, at Beijing Jiaotong University, China📖

Dr. Liang Gao is an Associate Professor at the School of Systems Science, Beijing Jiaotong University. He specializes in systems theory, complex networks, and data-driven transportation systems. With extensive teaching and research experience, Dr. Gao has contributed significantly to urban mobility, multi-layer network resilience, and intelligent transportation systems. His work has been recognized with numerous awards, including the China Intelligent Transportation Association Science and Technology Award (2019) and Beijing’s Scientific and Technological Progress Award (2020). He is also a member of prominent academic societies, such as the Chinese Society for System Engineering and the International Association for Complex Systems.

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Education Background🎓

Dr. Liang Gao completed his Bachelor of Science in Systems Engineering in 2002 from the Department of Systems Science, Beijing Normal University, China. He continued his academic journey at the same institution, earning a Master of Science in Systems Analysis and Integration in 2004. Driven by a passion for advancing theoretical and practical applications of systems science, he pursued and obtained his Ph.D. in Systems Theory in 2007. His doctoral research laid the foundation for his expertise in complex networks and data-driven systems analysis, establishing a robust academic base for his future endeavors.

Professional Experience🌱

Dr. Gao began his academic career as a Lecturer at Beijing Jiaotong University in 2007, where he taught and conducted research in systems science. In 2014, he was promoted to Associate Professor, reflecting his significant contributions to teaching, research, and academic leadership. During his tenure, he held visiting scholar positions at Northeastern University, USA, and the University of Aveiro, Portugal, where he advanced research on human mobility patterns and multi-layer transportation networks. Dr. Gao has delivered keynote talks at various prestigious international conferences and contributed to impactful projects, including urban mobility analysis and intelligent transportation system development, earning recognition for his expertise in complex systems and sustainable transportation.

Research Interests🔬

  • Complex Systems and Network Science
  • Urban Transportation and Mobility Analysis
  • Data-Driven Intelligent Transportation Systems
  • Resilience of Multi-Layer Networks
  • Systemic Approaches to Public Bicycle Scheduling

Author Metrics 

Dr. Gao has published extensively in high-impact journals such as Transportation Research Part B and PLoS ONE. His work has garnered significant citations, reflecting its influence in the fields of complex systems and transportation science. He also serves as a reviewer for prestigious journals and is actively engaged in academic collaborations worldwide.

Publications Top Notes đź“„

1. Switch between Critical Percolation Modes in City Traffic Dynamics

  • Year: 2019
  • Authors: Zeng, G., Li, D., Guo, S., Eugene Stanley, H., Havlin, S.
  • Journal: Proceedings of the National Academy of Sciences of the United States of America
  • Volume: 116
  • Issue: 1
  • Pages: 23–28
  • Abstract: This study explores the dynamics of urban traffic networks using percolation theory. It identifies transitions between two critical percolation modes, providing insights into traffic resilience under disruptions and aiding urban planning strategies.
  • Citations: 108
  • Access: Open Access

2. Weighted h-Index for Identifying Influential Spreaders

  • Year: 2019
  • Authors: Gao, L., Yu, S., Li, M., Shen, Z., Gao, Z.
  • Journal: Symmetry
  • Volume: 11
  • Issue: 10
  • Article: 1263
  • Abstract: This paper presents a weighted h-index metric designed to identify influential spreaders in complex networks. The study demonstrates its effectiveness compared to traditional measures in accurately identifying nodes with significant spreading capabilities.
  • Citations: 10
  • Access: Open Access

3. Identifying Influential Spreaders Based on Indirect Spreading in Neighborhood

  • Year: 2019
  • Authors: Yu, S., Gao, L., Xu, L., Gao, Z.-Y.
  • Journal: Physica A: Statistical Mechanics and its Applications
  • Volume: 523
  • Pages: 418–425
  • Abstract: This research proposes a novel method to identify influential spreaders by considering indirect spreading within their neighborhoods. The approach improves the understanding of influence dynamics in various networked systems.
  • Citations: 12

4. Critical Percolation on Temporal High-Speed Railway Networks

  • Year: 2022
  • Authors: Liu, Y., Yu, S., Zhang, C., Wang, Y., Gao, L.
  • Journal: Mathematics
  • Volume: 10
  • Issue: 24
  • Article: 4695
  • Abstract: The study applies percolation theory to temporal high-speed railway networks, analyzing their robustness and identifying critical points of failure. The findings contribute to enhancing the resilience of transportation systems.
  • Citations: 1
  • Access: Open Access

5. Cascading Failure with Preferential Redistribution on Bus-Subway Coupled Network

  • Year: 2021
  • Authors: Jo, S., Gao, L., Liu, F., Xu, L., Gao, Z.-Y.
  • Journal: International Journal of Modern Physics C
  • Volume: 32
  • Issue: 8
  • Article: 2150103
  • Abstract: This paper studies cascading failures in coupled bus-subway networks and introduces a preferential redistribution strategy to mitigate disruptions. The findings offer practical solutions for improving urban transit system resilience.
  • Citations: 12
Conclusion

Dr. Liang Gao’s outstanding contributions to systems science, his innovative approaches to urban mobility and network resilience, and his leadership in academic research make him a deserving candidate for the Excellence in Academic Research Award. His work demonstrates a clear alignment with the award’s objectives of recognizing excellence and fostering impactful research.

By expanding his interdisciplinary applications and deepening industry collaborations, Dr. Gao can further solidify his influence and pave the way for transformative advancements in intelligent transportation and complex systems. His trajectory reflects a blend of theoretical rigor and practical innovation, making him a role model for aspiring researchers in the field.