Urban Intelligence Training Program
Learning to work with AI in smart city contexts isn't just about understanding algorithms. It's about seeing how technology connects to actual urban challenges—traffic patterns nobody predicted, energy grids that behave unexpectedly, community needs that shift faster than planning cycles.
Our ten-month program starts in September 2025. We're building it around the real problems cities face, not textbook scenarios.
Applications open July 2025
What You'll Actually Learn
Five modules spread across forty weeks. Each one builds on the last, but they're designed so you can apply what you're learning while you're still in the program.
Urban Systems Foundation
Cities are complex. Before jumping into AI applications, we spend eight weeks understanding how urban infrastructure actually operates—and where it breaks down.
- Municipal data ecosystems and their limitations
- Transportation networks and real-time analysis
- Energy distribution patterns in diverse neighborhoods
- Public service delivery measurement
Machine Learning for Urban Data
City data is messy. Incomplete sensor readings, inconsistent reporting, gaps that span months. We focus on techniques that work with imperfect information.
- Pattern recognition in sparse datasets
- Predictive modeling with missing data points
- Time series analysis for infrastructure monitoring
- Bias detection in municipal data collection
Implementation Challenges
Theory meets reality here. Most AI projects in cities stall not because of technical problems but because of organizational complexity and community concerns.
- Working with legacy municipal systems
- Privacy considerations in public spaces
- Stakeholder communication across departments
- Budget constraints and phased rollouts
Case Study Analysis
Real projects from Boston, Seattle, and smaller municipalities. Some succeeded, others didn't. We examine both to understand what actually matters when deploying AI in urban settings.
- Traffic optimization failures and lessons
- Successful waste management automation
- Community feedback integration approaches
- Scaling from pilot to citywide deployment
Capstone Project
The final twelve weeks focus on a project of your choosing. Work with municipal data, build something functional, document what you learned—especially what didn't work as expected.
- Project scoping with real constraints
- Iterative development and testing
- Documentation for non-technical stakeholders
- Final presentation and peer review
Who Teaches This Program
Three practitioners who've spent years working directly with cities. They've dealt with bureaucracy, budget meetings, and systems that weren't supposed to fail but did anyway. Their approach is practical because it has to be.
Rashid Kimathi
Lead Instructor, Urban Systems
Spent eight years implementing traffic management systems across mid-sized cities. Started in data analysis, moved into system design after watching too many good projects fail due to poor implementation planning.
Gregor Novak
Technical Lead, Machine Learning
Former municipal IT director who transitioned into AI after realizing cities were collecting massive amounts of data without knowing what to do with it. Specializes in making complex models understandable to city planners.
Desmond Pike
Implementation Advisor
Works at the intersection of technology and community engagement. His projects focus on ensuring AI deployments actually serve neighborhood needs rather than just technical objectives. Blunt about what doesn't work.
Program Details
This isn't a quick certification course. Ten months is what it takes to properly understand both the technical aspects and the institutional realities of implementing AI in urban environments.
Weekly sessions combine remote lectures with hands-on project work. Three in-person workshops scheduled for October 2025, January 2026, and May 2026 in our Wales, MA facility.