Open Water Rates
Making water rates more accessible and understandable. 
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Benchmarking Energy & Water Use in New York City
The City of New York requires all large buildings to publicly disclose their annual energy consumption. As a graduate research assistant in the lab of Dr. Constantine Kontokosta I worked with this energy disclosure data in a number of projects. 
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publications
Web-Based Visualization and Prediction of Urban Energy Use from Building Benchmarking Data
Published in Bloomberg Data for Good Exchange 2015, 2015
Details two projects to increase both the availability and comprehensiveness of building energy data through interactive visualization and extrapolation with predictive models. Received ‘Best Paper’ award at the Bloomberg Data for Good Exchange 2015. Read more
Recommended citation: Kontokosta, C., Tull, C., Marulli, D., Pingerra, R., & Yaqub, M. (2015) Web-Based Visualization and Prediction of Urban Energy Use from Building Benchmarking Data. Presented at Bloomberg Data for Good Exchange 2015 New York, NY. https://www.researchgate.net/profile/Christopher_Tull/publication/282781435_Web-Based_Visualization_and_Prediction_of_Urban_Energy_Use_from_Building_Benchmarking_Data/links/561c6ab808ae6d17308b1843.pdf
How Much Water Does Turf Removal Save? Applying Bayesian Structural Time-Series to California Residential Water Demand
Published in KDD Workshop on Data Science for Food, Energy and Water, 2016
Monthly water savings from turf removal are estimated at the household level as the difference between actual usage and a synthetic control and then aggregated using a mixed-effects regression model to investigate the determinants of water savings. Read more
Recommended citation: Tull, C., Schmitt, E., Atwater, P., (2016). How Much Water Does Turf Removal Save? Applying Bayesian Structural Time-Series to California Residential Water Demand. Presented at the KDD Workshop on Data Science for Food, Energy and Water, San Francisco, CA. https://www.researchgate.net/publication/306219830_How_Much_Water_Does_Turf_Removal_Save_Applying_Bayesian_Structural_Time-Series_to_California_Residential_Water_Demand
A data-driven predictive model of city-scale energy use in buildings
Published in Applied Energy, 2017
We use statistical models to predict the energy use of 1.1 million buildings in New York City using the physical, spatial, and energy use attributes of a subset derived from 23,000 buildings required to report energy use data each year. Linear regression (OLS), random forest, and support vector regression (SVM) algorithms are fit to the city’s energy benchmarking data and then used to predict electricity and natural gas use for every property in the city. Model accuracy is assessed and validated at the building level and zip code level. Read more
Recommended citation: Kontokosta, C. E., & Tull, C. (2017). A data-driven predictive model of city-scale energy use in buildings. Applied Energy, 197, 303-317. http://www.sciencedirect.com/science/article/pii/S0306261917303835
talks
MindSoccer
Published:
Presented my capstone project for my B.S. in Computer Science: “MindSoccer” which was a project to navigate robotic vehicles using a brain-computer interface. This was my very first conference presentation so I was understandably nervous, but the crowd enjoyed our presentation and the accompanying demonstration with the vehicles and headsets. Read more
Web-Based Visualization and Prediction of Urban Energy Use from Building Benchmarking Data
Published:
Co-presented with my advisor to talk about our conference paper. Since we won the ‘Best Paper’ award, we also got to present the next day at Strata Hadoop World. This was quite fun, and we were set up in the middle of the Expo hall with a large screen. At the last second my advisor got stuck in traffic and couldn’t make it so I had to completely ‘wing’ his portion of the talk. I pulled it off but it was definitely a bit more exciting than I had planned! Read more
How Much Water Does Turf Removal Save?
Published:
Presented on behalf of my coauthors to talk about our conference paper on estimating the water savings attributable to turf removal programs. The workshop was an eclectic mix of topics, mainly from agriculture and water resources. The ag presence was heavy with a large showing from Iowa State and Purdue, likely because these were the institutions of the keynote speakers. Read more
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post. Read more
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post. Read more