Researcher, Xiamen University, China
🌟 Dr. Longbiao Chen is an Associate Professor at Xiamen University, China, specializing in ,crowdsensing ubiquitous computing, and large language models. With dual Ph.D. degrees from Sorbonne University, France, and Zhejiang University, China, he is a leader in applying advanced computing techniques to intelligent transportation, urban planning, and emergency response. His innovative work includes award-winning research on public transportation systems and urban event detection. Dr. Chen has authored numerous high-impact publications, earning accolades such as Best Paper Candidate at ACM UbiComp’15.
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Suitability For Best Researcher award
Dr. Longbiao Chen stands out as an exemplary candidate for a Best Researcher Award based on his significant contributions to the fields of crowdsensing, ubiquitous computing, and intelligent urban systems. Dr. Chen’s work not only pushes the boundaries of crowdsensing and ubiquitous computing but also sets benchmarks for sustainable urban innovation. His accolades, interdisciplinary impact, and vision firmly position him as a leader in his field and a deserving recipient of this prestigious recognition.
Education and Experience
- 🎓 Ph.D. in Computer Science (2015–2018)
Sorbonne University, France (Supervisors: Prof. Daqing Zhang and Prof. Thi-Mai-Trang Nguyen)
- 🎓 Ph.D. in Computer Science (2010–2016)
Zhejiang University, China (Supervisor: Prof. Gang Pan)
- 🎓 B.E. in Computer Science (2006–2010)
Chu Kochen Honors College, Zhejiang University, China
- 🧑🏫 Associate Professor
Xiamen University, China
Professional Development
🚀 Dr. Longbiao Chen’s Professional Contributions:
He has developed groundbreaking systems like CrowdBot, a robot management system enhancing campus services through hierarchical reinforcement learning. His expertise extends to urban planning, optimizing bike-sharing systems, and leveraging big data for intelligent transportation. Dr. Chen’s interdisciplinary projects address challenges in urban event detection, port logistics, and Internet of Things integration. His innovative ideas merge technology with real-world applications, such as the acclaimed “Twitting Coffee Machine,” linking home appliances to social networks, amassing over 30,000 followers within days. 📈 His vision transforms urban environments and fosters advanced computing applications.
Research Focus
🔬 Dr. Longbiao Chen’s Research Focus:
He excels in crowdsensing and ubiquitous computing, applying his expertise to diverse domains like intelligent transportation, emergency response, and urban planning. His research integrates cutting-edge techniques, including hierarchical reinforcement learning and multi-source data fusion, to address real-world challenges. 🏙️ His contributions include optimizing public transportation, detecting urban events, and enhancing disaster response through advanced urban data analytics. His work on robotics and autonomous systems leverages hybrid cloud-edge frameworks to improve campus services. Dr. Chen’s focus bridges technology with societal needs, setting benchmarks for sustainable urban innovation. 🌏
Awards and Honors
- 🏆 Best Paper Candidate Award, ACM UbiComp’15
- 📜 Accepted Paper, ACM UbiComp’16
- 📈 Published Research, ACM UbiComp’14 and IEEE T-ITS
- 🌟 Innovative Project Recognition, IEEE UIC’12 (Twitting Coffee Machine)
Publication Top Notes:
1. Dynamic Cluster-Based Over-Demand Prediction in Bike Sharing Systems
- Authors: L. Chen, D. Zhang, L. Wang, D. Yang, X. Ma, S. Li, Z. Wu, G. Pan, T.M.T. Nguyen
- Published In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
- Citations: 238 (as of 2016)
- Abstract:
This paper introduces a novel dynamic clustering-based framework to predict over-demand in bike-sharing systems. The method integrates spatial-temporal data analysis and machine learning to anticipate bike shortages and surpluses at stations, enhancing service efficiency.
2. Bike Sharing Station Placement Leveraging Heterogeneous Urban Open Data
- Authors: L. Chen, D. Zhang, G. Pan, X. Ma, D. Yang, K. Kushlev, W. Zhang, S. Li
- Published In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
- Citations: 171 (as of 2015)
- Abstract:
This research explores the optimization of bike-sharing station placement using heterogeneous urban data sources. By analyzing open data such as urban layout, traffic flows, and population density, the study provides a data-driven approach for efficient station distribution.
3. NationTelescope: Monitoring and Visualizing Large-Scale Collective Behavior in LBSNs
- Authors: D. Yang, D. Zhang, L. Chen, B. Qu
- Published In: Journal of Network and Computer Applications, Volume 55, Pages 170–180
- Citations: 171 (as of 2015)
- Abstract:
NationTelescope is a system designed to monitor and visualize collective behavior on a large scale using Location-Based Social Networks (LBSNs). The system captures mobility and activity patterns, providing valuable insights into population behavior across urban environments.
4. Container Port Performance Measurement and Comparison Leveraging Ship GPS Traces and Maritime Open Data
- Authors: L. Chen, D. Zhang, X. Ma, L. Wang, S. Li, Z. Wu, G. Pan
- Published In: IEEE Transactions on Intelligent Transportation Systems (T-ITS), Volume 17, Issue 5, Pages 1227–1238
- Citations: 118 (as of 2015)
- Abstract:
This paper presents a performance measurement framework for container ports by analyzing ship GPS traces and maritime open data. The method benchmarks port efficiency and identifies areas for operational improvement.
5. Deep Mobile Traffic Forecast and Complementary Base Station Clustering for C-RAN Optimization
- Authors: L. Chen, D. Yang, D. Zhang, C. Wang, J. Li
- Published In: Journal of Network and Computer Applications, Volume 121, Pages 59–69
- Citations: 109 (as of 2018)
- Abstract:
This study introduces a deep learning-based approach to forecast mobile network traffic and optimize base station clustering for Cloud Radio Access Networks (C-RAN). It addresses the challenges of managing massive mobile data traffic while ensuring efficient network operations.