Dr. Vivian W. H. Wong
College of Design, Construction and Planning
M.E. Rinker, Sr. School of Construction Management (50%)
Department of Urban and Regional Planning (50%)
"Developing intelligent, human-centric solutions to optimize the flow of people and resources in urban infrastructure systems."
Recent Updates
Poster abstract on dynamic mobility barrier detection accepted to ACM BuildSys'25 (Golden, CO).
Invited talk at the "Nigiwai: Placemaking Driven by Human Behavior" workshop at Stanford University.
Awarded the UF Research Opportunity Seed Fund (ROSF) as Lead PI for "Sensing for Empathetic Built Environments".
Paper on post-earthquake housing reconstruction simulation + RL accepted to ASCE i3CE 2025 (New Orleans, LA).
Paper on data-driven predictive modeling of pedestrian crowd flow accepted to IEEE Big Data 2024 (Washington, DC).
Research Areas
My research bridges the gap between AI/ML and practical implementation in civil infrastructure systems. We focus on two main pillars:
Sensing for Empathetic and Resilient People Mobility
We utilize computer vision and deep graph neural networks to understand how people move through the built environment. Moving beyond simple tracking, our lab focuses on identifying mobility barriers for wheelchair users and optimizing pedestrian flow for safety in crowded scenarios.
Simulation and Automation of Operations & Logistics
We apply reinforcement learning and systems modeling to solve complex resource allocation problems. Our work creates actionable algorithms for dynamic dispatchment, applicable to both smart manufacturing floors and post-disaster urban logistics.
Building End-to-End Automation Pipelines
We build full-stack automation pipelines: Data Ingest (Sensing/IoT) → Modeling (Machine Learning) → Evaluation → Deployment/Monitoring in simulators or real-world testbeds.
Publications
- Conf Dynamic Mobility Barrier Detection Using Wheelchair-mounted Sensors
- Conf PyRebuild: A Python-Based Simulator For the Dynamic Post-Earthquake Housing Reconstruction Problem
- Conf Enhancing Data-Driven Predictive Modeling of Pedestrian Crowd Flow with Spatial Priors
- Journal Identification and Interpretation of Melt Pool Shapes in Laser Powder Bed Fusion with Machine Learning
- Journal Generating Dispatching Rules for the Interrupting Swap-Allowed Blocking Job Shop Problem
- Journal Fusion of CCTV Video and Spatial Information for Automated Crowd Congestion Monitoring
- Conf Modeling Crowd Data and Spatial Connectivity as Graphs for Crowd Flow Forecasting
- Journal Segmentation of Additive Manufacturing Defects Using U-Net
Teaching
I believe in providing students with an adaptable, interdisciplinary skillset through hands-on labs and intuitive understanding of engineering systems, and quantitative input-output analysis.
AI in the Built Environment (DCP 4300)
Fall 2025 | University of Florida
View Student Final ProjectsIntro to Planning Information Systems (URP 4273)
Fall 2025 | University of Florida
Intro to Planning Information Systems (URP 4273/6270)
Fall 2024 | University of Florida
Academic Service
- Publication Chair: ACM BuildSys 2025
- Journal Reviewer: IEEE TNNLS, Engineering Applications of AI, Automation in Construction, Optimization Letters.
Research Team
Status: I am not currently hiring for Ph.D. researcher positions. If you are curious about research and want to learn more about it, please email me. Self-funded postdoctoral scholars interested in research with either Rinker or URP may also email me.
[Archived opening post 2024]: I am looking for passionate and curious minds to join my lab. An application to either the URP or Construction concentration for DCP Ph.D. can be submitted at DCP Ph.D. Admissions. DCP Ph.D positions are STEM designated degrees and are fully funded through RA and TA for the duration of the studies (~4 years). Prospective students should meet UF DCP PhD admission requirements, and have strong math/stats/programming background and passion for applied AI/ML research in the domain of urban societal systems. I also encourage students to pursue a PhD minor or another MS degree in CS/stats/EE, as well as summer internships in the industry, throughout the duration of the PhD. To express your interest in the open positions before submitting a full application, prospective students are encouraged to fill out this pre-application interest form to list relevant experiences, career objectives, and relevant linkedin/github pages. Afterwards, please email me indicating that you have completed the form, with the subject starting with "Prospective Graduate Student". I HIGHLY RECOMMEND reading my Research Statement and previous publications before emailing me.
Current Advisees
-
Ao He (Ph.D. Student)
Research: Bicyclist safety, behavior, and flow patterns; computer vision.
aohe@ufl.edu -
Qianchen Yu (Ph.D. Student)
Research: Post-disaster rebuild modeling and simulation; reinforcement learning.
Website | yu.qianchen@ufl.edu -
Isabelle Gatmaitan (Undergraduate)
Research: Empathetic built environment and sensing.
igatmaitan@ufl.edu
Miscellaneous
Personal
I was born in the lovely city of Toronto, Canada and a permanent resident of the U.S.. I also spent a delightful amount of my childhood in Hong Kong and Beijing.
Table Tennis
I am a passionate competitive table tennis player. Beyond playing, I serve the community as:
- Tournament Director: Ocala Table Tennis Club. I organize tournaments and manage the club's web presence.
- Faculty Advisor: University of Florida Table Tennis Club.
- Formerly player for Stanford Table Tennis Team (2022-2024).
Curriculum Vitae
Education
-
Ph.D. in Civil Engineering (2024)
Stanford University | Advisor: Kincho H. Law -
M.S. in Civil Engineering (2019)
Stanford University -
B.S. in Civil Engineering (2017)
University of Illinois at Urbana-Champaign
Academic Appointments
-
Assistant Professor (2024 - Present)
University of Florida
Industry Experience
2022
Amazon Science (Applied Scientist Intern)
Developed models for evaluating brands in Softlines Discovery.
2018
Alibaba City Brain (Research Intern)
Contributed to large-scale urban data initiatives for city management.
Developed models for evaluating brands in Softlines Discovery.
Contributed to large-scale urban data initiatives for city management.