The Urban Intelligence Lab at Michigan Technological University is dedicated to shaping smarter, more responsive urban environments through innovative technology and data-driven insights.
The Urban Intelligence Lab Enterprise is a hands-on, interdisciplinary team where students design, build, and deploy data-driven solutions for smarter, more sustainable communities. We combine sensing, analytics, and system-level thinking to tackle real challenges on campus and in the Keweenaw region.
Develop and deploy computer vision systems alongside other sensors to detect, classify, and track activity in real environments.
Use data from sensors and computer vision to understand how people and resources move through public open spaces and infrastructure. Translate these insights into recommendations and strategies that guide the design, planning, and management of parks, transit corridors, and other community assets.
Use machine learning and analytics to turn raw sensor and vision data into actionable insights for decision-makers in communities and industry partners.
Collaborate across data science, computer science, and diverse engineering fields—alongside social science and human-factors expertise—to design, test, and deploy solutions that are technically sound and responsive to community needs.
This project uses computer vision to detect, track, and map how students move through campus and interact with its infrastructure. By generating data on traffic patterns, gathering spots, and asset usage, the system provides a clearer picture of campus dynamics. These insights directly support the Alternative Energy Enterprise in identifying optimal locations for renewable energy panels, ensuring smarter and more efficient deployment.
Working with the Great Lakes Research Center, the team is developing computer vision systems to track and classify vessels traveling through the Portage Canal. This work will expand to monitor fishing activity at remote locations like Stannard Rock, where collecting reliable data has historically been difficult. By producing high-quality usage data, the project strengthens fisheries research and supports collaborative management with tribal and state partners, ultimately benefiting recreational anglers and regional ecosystems alike.
Interests: Smart and Connected Cities, Self-efficacy in STEM undergraduates
Interests: Data Analysis, Database Management and Design, Statistics Education, Human Factors
Interests: Machine Learning for Health and Urban Data, Data Analysis and Visualization, Applied Statistics
Interests: Mathematics, AI Ethical Usage and Integration
Interests: CV model training and evaluation, Autonomous Flight: Propulsion systems and micro jet engines, CAD Modeling: Onshape, SolidWorks
Interests: Data Analytics, Algorithms, Predictive Modeling
Interests: Prosthetics, Computational Biology, Machine learning
Interests: Beach, Sticks, Biting
Please reach out if you are interested in learning more about our projects, collaborating with the Urban Intelligence Lab, or joining our team. Let us know who you are and your areas of interest!