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CompSustNet is a research network sponsored by the National Science Foundation through an Expeditions in Computing award. Thirteen U.S. academic institutions led by Cornell University, along with many national and international collaborators, are exploring new research directions in computational sustainability.

Interdisciplinary, multi-investigator research teams are focusing on cross-cutting computational topics such as optimization, dynamical models, big data, machine learning, and citizen science. These methods are being applied to sustainability challenges including conservation, poverty mitigation and renewable energy.

CompSustNet builds on the work of the Institute for Computational Sustainability (ICS), which started the field through one of the first NSF Expeditions awards in 2008. The virtual research lab includes educational, community building, and outreach activities to ensure that computational sustainability becomes a self-sustaining discipline.

CompSustNet research areas



[Video] AI for Earth

Microsoft Research Faculty Summit 2017: The Edge of AI - Jul 18, 2017


Jon M. Conrad (2018). Real Options for Endangered Species. (To appear) Ecological Economics. doi: 10.1016/j.ecolecon.2017.07.027.

Natalie M Mahowald, Daniel S Ward, Scott C Doney, Peter G Hess, James T Randerson (2017). Are the impacts of land use on warming underestimated in climate policy?. Environmental Research Letters. doi: 10.1088/1748-9326/aa836d. [pdf]

Di Chen, Yexiang Xue, Daniel Fink, Shuo Chen, Carla P. Gomes (2017). Deep Multi-species Embedding . Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17). doi: 10.24963/ijcai.2017/509. [pdf]

Xiaojian Wu, Yexiang Xue, Bart Selman, Carla P. Gomes (2017). XOR-Sampling for Network Design with Correlated Stochastic Events. Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17). doi: 10.24963/ijcai.2017/647. arxiv: 1705.08218. [pdf]

Mark D. Reynolds, Brian L. Sullivan, Eric Hallstein, Sandra Matsumoto, Steve Kelling, Matthew Merrifield, ... Scott A. Morrison (2017). Dynamic conservation for migratory species. Science Advances. doi: 10.1126/sciadv.1700707.

Sample Projects

Materials Discovery
Phase map identification problem

Photo: John Gregoire (JCAP/Caltech)

What: Rapid characterization of crystal structures from high-throughput X-ray diffraction experiments.
Why: Identify new materials for fuel cells, energy storage, and solar fuel generation.
How: Pattern decomposition, constraint and probabilistic reasoning, crowdsourcing.

Smart Grid
Solar farm

Photo: DOE

What: Power grid modeling, control, and energy storage.
Why: Managing the power system with increasing use of renewable sources of electricity.
How: Stochastic optimization, sequential decision making, pattern decomposition.

Big Data for Africa
Weather station installation

Photo: Frank Annor (TAHMO)

What: Deploy 20,000 low-cost weather stations across Africa.
Why: Improve weather predictions, which is directly related food security.
How: Optimal placement, bayesian networks, multi-scale probabilistic modeling.

Landscape-Scale Conservation
Andean Bears

Photo: Santiago Molina

What: Socio-ecological corridor in the Ecuadorian Andes.
Why: Protect endangered Andean bear and other species in a significant biodiversity hotspot, while improving livelihoods of local communities.
How: Spatial capture-recapture, stochastic optimization, spatio-temporal modeling.

Green Security Games
Anti-peaching patrol simulation

Photo: USC Teamcore

What: Protection Assistant for Wildlife Security (PAWS).
Why: Provide randomized patrol routes to combat poaching activity and protect wildlife.
How: Game theory-based analysis, spatio-temporal analysis, human behavior modeling, optimization.

Microbial Fuel Cells

Photo: Hong Liu (OSU)

What: Planning Algorithms for Resource Constrained Experimental Design.
Why: Efficiently identify biological and physical characteristics that maximize energy production from wastewater treatment.
How: Bayesian response surface modeling, budgeted optimization, simulation matching.

Examples of cross-cutting computational themes and projects

Computational themes and interactions