Boyu Wang
Education:
B.Sc., Physics, 2010, National University of Singapore, Singapore.
M.Sc., Physics, 2015, National University of Singapore, Singapore.
Research Interests: Agent-Based Modeling, Geographic Information Science, Machine Learning, Urban Simulation
Grants and Awards:
Travel Grant Award, 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022)
Best Paper Award, 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation (GeoSim 2022)
Government-Linked Company Undergraduate Scholarship (SM3), Ministry of Education, Singapore (2006)
Bio:
Boyu Wang is a PhD student in the Department of Geography at the University at Buffalo, advised by Prof. Andrew Crooks. His research interests include agent-based modeling, geographic information science, machine learning, and urban simulation. He received both his MSc and BSc in Physics from National University of Singapore. Prior to his PhD study, he worked as a data scientist at the APAC Innovation Center in Hewlett Packard Enterprise (HPE) in Singapore. He also worked as a R&D engineer at TUMCREATE and Singapore-MIT Alliance for Research and Technology (SMART) Centre, and as a software engineer at Cognizant.
Publications:
Jiang, N., Yin, F., Wang, B., and Crooks, A. (2024). A Large-Scale Geographically Explicit Synthetic Population with Social Networks for the United States. Scientific Data. https://doi.org/10.17605/OSF.IO/FPNC2
Chen, Q., Wang, B., and Crooks, A. (2024). Community Resilience to Wildfires: A Network Analysis Approach by Utilizing Human Mobility Data. Computers, Environment and Urban Systems, 110, 102110. https://doi.org/10.1016/j.compenvurbsys.2024.102110
Jiang, N., Crooks, A., Yin, F., and Wang, B. (2023). Geographically-Explicit Synthetic Populations for Agent-Based Models: A Gallery of Applications. In Proceedings of the 2023 Conference of the Computational Social Science Society of the Americas.
Wang, B. and Crooks, A. (2023). Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning (Short Paper). In Proceedings of the 12th International Conference on Geographic Information Science (GIScience 2023) https://doi.org/10.4230/LIPIcs.GIScience.2023.81
Wang, B., Hess, V., and Crooks, A. (2022). Mesa-Geo: A GIS Extension for the Mesa Agent-Based Modeling Framework in Python. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation (pp. 1-10). https://doi.org/10.1145/3557989.3566157
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