In a follow-on from my summer class at Vanderbilt Summer Academy, I joined the Programs for Talented Youth (PTY) for a one-day version of my course Sensors and Big Data Analysis in the Weekend at Vanderbilt University (WAVU) program. The course covered electrical engineering of sensor prototypes, data collection and analysis, and microcontroller programming.

At a middle school STEM day hosted by Vanderbilt student chapter of the American Society of Civil Engineers, I had the opportunity to conduct a hands-on activity focused on bike infrastructure planning with over 100 students from Metro Nashville Public Schools. Students pretended to be urban planners with the challenging task of multimodal infrastructure planning with limited resources.

The event was covered by the Vanderbilt School of Engineering in a recent article.

Topics in sensor deployments, smart cities, and data analysis were all part of a class I taught at Vanderbilt Summer Academy titled “Sensors and Big Data Analysis”. I had the opportunity to design this course and teach gifted high school students from across the United States and abroad during four weeks of class and 120 hours of instruction. Students learned about electrical engineering and building their own sensor prototypes, programming microcontrollers for data collection and control tasks, and data analysis techniques in spreadsheets and Python.

Image credit: Vanderbilt Programs for Talented Youth

Along with Prof. Work, Prof. Philip, Erin Hafkenschiel, and Leigh Shoup, I co-authored an opinion article in the Tennessean, discussing the use of electric scooters in Nashville and the larger mobility challenges of the city. The op-ed is titled Scooters are here to stay in Nashville. We have to make it work. It highlights the experiences of Vanderbilt in dealing with mobility challenges around campus, and how this insight might help shape the city’s future transportation approach.

Read the article here.

I had the opportunity to travel to Norrkoping, Sweden, to present a new paper titled ‘Data reconciliation of freight rail dispatch data’ to an audience of rail operations experts at the 8th International Conference on Railway Operations Modelling and Analysis (RailNorrkoping). The work details the process for constructing an automated data reconciliation pipeline from any optimization-based dispatching model. This allows for the identification and correction of infeasible dispatching data at the network scale, which is a difficult and costly task even at small scales.

Image credit: Chris Barkan, University of Illinois Urbana-Champaign