My research focuses in modeling and understanding the complex nature of urban interrelationships both at the social and infrastructure levels. I leverage Machine Learning, Visualization, and GIS to accomplish this with the ultimate goal of achieving a better understanding of urban dynamics to reach more sustainable cities.
Time series clustering of electricity data leads to the emergence of spatial aggregation. Commute times are used to explain this phenomenon, showing that long transit trips are the strongest predictors.
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Developed a sentiment analysis model to capture people's perception on working from home. Then used the sentiment and other Twitter (now X) extracted features to estimate current telecommuting prevalence in the US at the national and state level.
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Investigating the way in which people use electricity in Chicago
Ongoing research...
I work as part of the Transportation Systems & Mobility group in analyzing transportation system management strategies involving emerging vehicle and information technologies. I specifically work on:
Worked as part of the team that did the city-wide trip survey and developed the subsequent transportation EMME model in Bogota using the 4-step approach. This model is currently being used as one of the main inputs for urban planning and policy making in the city.