Nicholas is currently a Ph.D candidate in Urban Science at the University of Warwick's Institute for the Science of Cities and an Assistant Research Scientist at NYU's Center for Urban Science and Progress. He obtained a Masters degree from NYU's Interactive Telecommunications Program in 2013 and launched Open Trash Lab - a living laboratory that seeks to create collaborative opportunities to explore and understand the impact and pervasiveness of urban waste. His current research is focused on the design and development of cyber-physical systems to quantify the urban environment and novel data-driven approaches to understand urban phenomena including waste generation, urban mobility and environmental processes.


  • Digital Footprints: Using Wi-Fi Probe and Locational Data to Analyze Human Mobility Trajectories in Cities.  [link]

    Traunmueller, M., Johnson, N. E., Malik, A., & Kontokosta, C. E. (2018) Computers, Environment and Urban Systems

  • Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment  [link]

    Johnson, N. E., Bonczak & Kontokosta C. E. (2018) Atmospheric Environment 184, 9-16

  • Using machine learning and small area estimation to predict building-level municipal solid waste generation in cities  [link]

    Kontokosta, C. E., Hong, B., Johnson, N. E. & Starobin, D. (2018) Computers, Environment and Urban Systems

  • Digital Traces: Modeling Urban Mobility using Wifi Probe Data  [.pdf]

    Traunmueller, M., Johnson, N. Malik, A., & Kontokosta, C. E. 6th International Workshop on Urban Computing, ACM KDD 2017, Halifax, Nova Scotia, Canada

  • Understanding Neighborhood Dynamics Through Low-cost In-situ Sensing of the Urban Troposphere

    Kontokosta, C. E., & Johnson, N. (2017). American Meteorological Society’s 13th Symposium of the Urban Environment

  • Urban phenology: Toward a real-time census of the city using Wi-Fi data  [.pdf]

    Kontokosta, C. E., & Johnson, N. (2017). Computers, Environment and Urban Systems, 64, 144-153.

  • Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City  [.pdf]

    Johnson, N. E., Ianiuk, O., Cazap, D., Liu, L., Starobin, D., Dobler, G., & Ghandehari, M. (2017). Waste Management, 62, 3-11

  • Landfill Hunter: Learning about Waste through Public Participation  [.pdf]

    Johnson, N. E., & Grey, F. (2016). Human Computation, 3(1), 243-252

  • The Quantified Community at Red Hook: Urban Sensing and Citizen Science in Low-Income Neighborhoods  [.pdf]

    Kontokosta, C. E., Johnson, N. E. & Schloss, A. (2016). Proceeding of the 2016 Bloomberg Data for Good Exchange