Research interests
Reinforcement learning–driven resource allocation in complex urban systems
Ph.D. student in Urban and Regional Planning at the University of Florida — working on AI-driven simulation and reinforcement learning for post-disaster housing recovery, urban resilience, and construction scheduling.
Research
PyRebuild: Simulation and Deep RL for Post-Disaster Housing Reconstruction
PyRebuild is a Python-based discrete-event simulator that models post-earthquake housing reconstruction under resource constraints and uncertainty. It serves as an AI-ready testbed for studying how different scheduling policies affect system-wide recovery, regional disparities, and household-level fairness.
- Models batched damage reporting, dynamic crew mobilization, and lognormal repair durations calibrated to real events (e.g., the 2018 Lombok earthquake).
- Trains PPO-based reinforcement learning policies (Stable-Baselines3) for crew allocation and compares them to rule-based baselines (LJF, SJF, random).
- Encodes fairness through waiting-time-based rewards and candidate ordering, enabling analysis of efficiency–equity trade-offs.
- Explores transfer-learning setups where policies learned from one region are adapted to regions with different damage structures and resource levels.
Academics
- Research on post-disaster housing recovery, urban resilience, and RL-driven resource allocation.
- Developing the PyRebuild simulator and PPO-based policies for reconstruction scheduling.
- Concentration in urban data science and spatial analysis, with strong emphasis on Python-based spatial methods and GeoAI.
- Training in urban economics, social inclusion, real-estate analysis, and multi-scale planning and design.
Conferences & Workshops
CRC&CI 2026 (ASCE)
Deep RL-based Transferable Scheduling for Post-Disaster Housing Reconstruction
Oral · Construction Research Congress & Computing in Civil Engineering · San Antonio, USA · March 2026 (forthcoming)
- Extends PyRebuild with deep RL under stochastic disruptions and explores cross-region policy transfer.
ACSP 2025
Adaptive Post-Disaster Housing Reconstruction Scheduling: A Deep Reinforcement Learning Approach
Oral · Association of Collegiate Schools of Planning Annual Conference · Minneapolis, USA · October 2025
- Discusses adaptive reconstruction schedules under changing resource and policy scenarios from a planning perspective.
i3CE 2025 (ASCE)
PyRebuild: A Simulation Framework for AI-Driven Adaptive Resource Allocation in Post-Disaster Housing Recovery
Talk · International Conference on Computing in Civil Engineering · New Orleans, USA · May 2025
- Introduces the PyRebuild framework and evaluates rule-based scheduling strategies under incomplete information.
AAAI 2025 AI4UP Workshop
PyRebuild: A Python-Based Simulator For Dynamic Post-Earthquake Reconstruction
Poster · AI for Urban Planning (AI4UP) · Philadelphia, USA · March 2025
- Presents PyRebuild as an AI-ready testbed for RL-based scheduling and fairness-aware resource allocation.
Experience
- Contributed to a geospatial visualization framework for traffic–human–environment studies.
- Built multi-source spatial datasets linking socio-economic indicators with road networks and land-use layers.
- Assisted in building urban spatial databases for site analysis and urban renewal projects.
- Developed mapping workflows to turn analytical results into clear maps and graphics for planning practice.
- Collected housing transaction and income data for eight major Chinese cities.
- Constructed a housing affordability database and performed basic descriptive analysis and visualization.
Skills
Programming & ML / RL
- Python (NumPy, pandas, PyTorch).
- Stable-Baselines3 (PPO, DQN) for RL training on custom simulation environments.
- R for statistical modeling and regression analysis.
- Git-based workflows for experiment tracking and reproducible pipelines.
Data & Spatial Analysis
- Data cleaning, descriptive statistics, basic modeling, and visualization.
- ArcGIS Pro and QGIS for spatial data processing, mapping, and urban analysis.
- Integration of socio-economic indicators with spatial units for planning and policy questions.
Research & Communication
- Experience writing research briefs, academic-style reports, and slides in English and Chinese.
- Comfortable explaining technical results to planning, policy, and interdisciplinary audiences.
Publications
Yu, Q., Alisjahbana, I., & Wong, V. W. H. (2025). PyRebuild: A Simulation Framework for AI-Driven Adaptive Resource Allocation in Post-Disaster Housing Recovery. In Proceedings of the ASCE International Conference on Computing in Civil Engineering (i3CE 2025), New Orleans, USA.
Yu, Q., Alisjahbana, I., & Wong, V. W. H. (2026). Optimizing Post-Disaster Housing Reconstruction Scheduling under Stochastic Disruptions using Deep Reinforcement Learning. Accepted for publication in the ASCE Construction Research Congress & Computing in Civil Engineering (CRC&CI 2026) conference proceedings, San Antonio, USA.
Full text coming soon (camera-ready in preparation).
Let’s connect
I am happy to discuss collaborations or internships at the intersection of urban resilience, AI-driven simulation, reinforcement learning, and social/urban computing.