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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

 

Upcoming Events

USC CAIS 2018 Summer Visiting Fellows Program

Eco-Informatics Summer Institute (EISI)

Videos

Oceanic plastic trash conveys disease to coral reefs


Cornell Chronicle (CornellCast) - Jan 25, 2018

Related

Publications

Jonathan M. Gomes-Selman, Qinru Shi, Yexiang Xue, Roosevelt García-Villacorta, Alexander S. Flecker, Carla P. Gomes (2018). Boosting Efficiency for Computing the Pareto Frontier on Tree Structured Networks. (To appear) CPAIOR 2018: Integration of Constraint Programming, Artificial Intelligence, and Operations Research. doi: 10.1007/978-3-319-93031-2_19.

Junwen Bai, Sebastian Ament, Guillaume Perez, John Gregoire, Carla Gomes (2018). An Efficient Relaxed Projection Method for Constrained Non-negative Matrix Factorization with Application to the Phase-Mapping Problem in Materials Science. (To appear) CPAIOR 2018: Integration of Constraint Programming, Artificial Intelligence, and Operations Research. doi: 10.1007/978-3-319-93031-2_4.

Hangil Chung, Daniel Freund, David B. Shmoys (2018). Bike Angels: An Analysis of Citi Bike's Incentive Program. Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS '18). doi: 10.1145/3209811.3209866. [pdf]

Qinru Shi, Jonathan M. Gomes-Selman, Roosevelt García-Villacorta, Suresh Sethi, Alexander S. Flecker, Carla P. Gomes (2018). Efficiently Optimizing for Dendritic Connectivity on Tree-Structured Networks in a Multi-Objective Framework. Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS '18). doi: 10.1145/3209811.3209878. [pdf]

Amrita Gupta, Caleb Robinson, Bistra Dilkina (2018). Infrastructure Resilience for Climate Adaptation. Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS '18). doi: 10.1145/3209811.3209859. [pdf]

Caleb Robinson, Bistra Dilkina (2018). A Machine Learning Approach to Modeling Human Migration. Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS '18). doi: 10.1145/3209811.3209868. [pdf]

Srinivasan Iyengar, Stephen Lee, Daniel Sheldon, Prashant Shenoy (2018). SolarClique: Detecting Anomalies in Residential Solar Arrays. Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS '18). doi: 10.1145/3209811.3209860. [pdf]

Elizabeth Bondi, Lucas Joppa, Milind Tambe, Debadeepta Dey, Ashish Kapoor, Jim Piavis, ... Arvind Iyer (2018). AirSim-W: A Simulation Environment for Wildlife Conservation with UAVs. Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS '18). doi: 10.1145/3209811.3209880. [pdf]

Anna X. Wang, Caelin Tran, Nikhil Desai, David Lobell, Stefano Ermon (2018). Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data. Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS '18). doi: 10.1145/3209811.3212707. [pdf]

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