Automatically Tracking and Executing Green Actions

We believe that self-reporting is a limiting factor in the original vision of, and this component of our research has begun to explore alternatives. For example, we showed that financial data can be used to extract footprint information [1], and in collaboration with researchers at Intel and University of Washington, we used a mobile device to track and visualize green transportation behavior in the Ubigreen project (published at CHI 2009 [2]). We have also worked on algorithms to predict the indoor location of work and home arrival time of residential building occupants so as to automatically minimize thermostat use [3, 4]. Finally, we moved away from individual behavioral remedies to structural remedies by exploring tools that could help tenants to pick greener apartments [5]

[1] J. Schwartz, J. Mankoff, H. Scott Matthews. Reflections of everyday activity in spending data. In Proceedings of CHI 2009.  (Note). (pdf)

[2] J. Froehlich, T. Dillahunt, P. Klasnja, J. Mankoff, S. Consolvo, B. Harrison, J. A. Landay, UbiGreen: Investigating a Mobile Tool for Tracking and Supporting Green Transportation Habits. In Proceedings of CHI 2009. (Full paper) (pdf)

[3] Indoor-ALPS: an adaptive indoor location prediction system Christian Koehler, Nikola Banovic, Ian Oakley, Jennifer Mankoff, Anind K. Dey
UbiComp ’14 Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014

[4] TherML: occupancy prediction for thermostat control Christian Koehler, Brian D. Ziebart, Jennifer Mankoff, Anind K. Dey UbiComp ’13 Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, 2013

[5] Jennifer Mankoff, Dimeji Onafuwa, Kirstin Early, Nidhi Vyas, Vikram Kamath Cannanure: Understanding the Needs of Prospective Tenants. COMPASS 2018: 36:1-36:10