Empowering the next generation of digital urbanists.
My approach to teaching is rooted in the principles of Open Science and Computational Thinking. I believe that urban planning in the 21st century requires a deep understanding of digital tools, not just as consumers, but as creators.
I focus on bridging the gap between theoretical urban models and practical software implementation, ensuring students can translate complex urban challenges into scalable digital solutions.
University Courses
Core curriculum and advanced electives taught at the Graduate School of Planning.
Guest Lectures & Workshops
Shorter teaching engagements, invited sessions, and workshop formats.
Machine Learning
Digital Futures
Global workshop teaching basic machine learning workflows through a participant-led final project.
Learning from Design Heritage: Investigation of Data-driven Methods
MIT
Guest lecture and tutorial session built around a serious board game assignment for design heritage learning.
Feeling Architecture: Affective Computing and Digital Heritage
MIT
Practice session on stigmergy within a course on affective computing and digital heritage.
Workshops & Coding Cafés
Short-form training and informal peer-learning sessions for research software.
Open Science for Urban Researchers
March 2024Hands-on training on reproducible research workflows using R and Quarto.
Coding Café: Spatial Data Science
MonthlyInformal peer-learning sessions for troubleshooting spatial analysis scripts.
Open Materials
Student Supervision
Machine Learning for Urban Heat Island Mitigation
Agent-Based Modeling of Pedestrian Movement in Transit Hubs
Teaching Innovations
Experimental methods and tools I've developed to enhance the learning experience.
VR Field Trips
Using VR to explore urban planning case studies globally without leaving the classroom.
Auto-Grader for GIS
Developed a custom Python framework for instant feedback on spatial analysis assignments.
Collaborative Mapping
Real-time multi-user mapping exercises using custom web-based GIS tools.