Sharing knowledge and inspiring the next generation of transportation engineers and researchers.
I believe in hands-on, project-based learning that bridges theory and real-world applications. My courses emphasize practical skills, critical thinking, and the use of modern tools to solve complex transportation challenges. Students learn by doing - building actual tools, analyzing real data, and contributing to meaningful research.
Introduction to artificial intelligence and machine learning applications in transportation systems. Covers predictive modeling, computer vision for traffic analysis, and LLM applications for transportation research.
Explores sustainable transportation planning, electric vehicle infrastructure, public transit optimization, and policy frameworks for reducing urban transportation emissions.
Hands-on course covering traffic microsimulation using SUMO, network modeling, scenario analysis, and digital twin concepts for transportation systems.
Free educational materials and tutorials for students and researchers.
Step-by-step video guides on SUMO, Python for transportation, and AI tools development.
Coming SoonCurated transportation datasets for practice including GTFS, traffic counts, and survey data.
Browse DatasetsReady-to-use Python notebooks and code templates for common transportation analyses.
View on GitHubSuggested research and course project topics with guidance and resources.
Explore IdeasInstallation and setup guides for SUMO, Python, QGIS, and other transportation tools.
View GuidesSpecialized training sessions and invited talks.
A hands-on workshop covering SUMO basics, network creation, and running your first simulation. Open to all students and researchers.
Invited seminar discussing the latest AI applications in sustainable transportation, including case studies from ongoing research projects.
Practical workshop on creating interactive web applications for transportation research using Python and Streamlit.