Computational Sustainability (CompSust) Track

CALL FOR PAPERS

Computational Sustainability aims to apply computational techniques to the balancing of environmental, economic, and societal needs, in order to support sustainable development and a sustainable future. Research in computational sustainability is inherently interdisciplinary: It brings together computational sciences and other fields such as environmental sciences, biology, economics, and sociology. AI techniques and methodologies can be instrumental in addressing sustainability problems and questions, for example to increase the efficiency and effectiveness of the way we manage and allocate our natural and societal resources. This will also enrich and transform AI, by providing new challenges.
Sustainability domains include the following:

• Natural resources and the environment (such as water, atmosphere, oceans, forest, land, etc.)
• Economics and human behavior (such as well-being, poverty, diseases, over-population, etc.)
• Energy resources (for example, renewable energy, smart grid, and so on.)
• Human-built systems and land use (such as transportation, cities, buildings, agriculture, etc.)
• Climate (such as climate prediction, impact of and on climate, etc.)

This special track is dedicated to papers concerned with innovative notions, models, algorithms, techniques, methodologies, and systems, in order to address problems in computational sustainability. Papers can range from formal analysis to applied research. Papers describing interesting sustainability problems and data sets, or papers proposing general challenges and competitions for computational sustainability, are also welcome.
The CompSust track welcomes three types of articles:

• Technical papers, showing how AI can be instrumental in addressing sustainability questions;
• Emerging computational sustainability applications
• Data challenge papers providing the description of a new sustainability problem as well as the corresponding data set to be made available to the AI community.  

Technical papers must follow the instructions given in the general call for technical conference papers. Dataset papers should be no longer than four pages in length, included figures and references. Please indicate that your paper is a dataset paper or an emerging application paper by selecting this paper type in the submission site.

Submission Rules

Submission Link: http://ijcai15-cs.confmaster.net/

We do not accept supplementary materials to extend the paper in IJCAI 15

IJCAI 2015 Computational Sustainability Track chairs:

Carla Gomes (This email address is being protected from spambots. You need JavaScript enabled to view it.)

IJCAI2015 Computational Sustainability Awards

IJCAI 2015 is joining with the Institute for Computational Sustainability (ICS) to promote work at the intersection of computing and sustainability on principles and applications that address environmental, economic, and societal needs in support of a sustainable future. A 2015 Computational Sustainability Track committee will select outstanding papers in this area to receive ICS travel awards to the authors of the IJCAI/CS Track 2015 CompSust Track Outstanding papers (Regular and Student award).

Track Chairs

Carla Gomes, Cornell University

 Area Chairs

Rogers, Alex, University of Southamption
Dilkina, Bistra, Georgia Institute of Technology
Krause, Andreas, ETH Zurich
Ermon, Stefano, Stanford University

Program Committee

Albore, Alexandre, Onera
Au, Tsz-Chiu, Ulsan National Institute of Science and Tech.
Bejar Torres, Ramon, Universitat de Lleida
Bent, Russel, Los Alamos National Laboratory
Brown, Ken, University College Cork
Brown, Matthew, University of Southern California
Chades, Iadine, CSIRO
Crowley, Mark, University of Waterloo
Damoulas, Theodoros, NYU
Das, Kamalika, NASA Ames Research Center
Decker, Keith, University of Delaware
Ding, Wei, University of Massachusetts
Dujardin, Yann, CSIRO
Faghmous, James, University of Minnesota
Fang, Fei, University of Southern California
Fenet, Serge, Universite Lyon 1
Fernandez, Cesar, University of Lleida
Fink, Daniel, Cornell University
Fisher, Doug, Vanderbilt University
Gao, Alice, University of British Columbia
Gavanellii, Marco, Univ. of Ferrara
Hossain, Shahriar, Univ Texas El Paso
Kelly, Jack, Computing Department, Imperial College London
Kersting, Kristian, TU Dortmund University
Kolter, J., Carnegie Mellon University
Kumar, Vipin, University of Minnesota
Law, Edith, University of Waterloo
Le Bras, Ronan, Cornell University
Leuker, Martin, University of Luebeck
Li, Jason, The Australian National University
Liu, Yan, USC
Lombardi, Michele, University of Bologna
Low, Bryan Kian Hsiang, National University of Singapore
Lubin, Benjamin, Boston University
Mackworth, Alan, University of British Columbia
Marwah, Manish, HP Labs
Mateu, Carles, Univ. Lleida
Milano, Michela, DISI Universita’ di Bologna
Natarajan, Sriraam, Indiana Univ
Nguyen, Thanh, University of Southern California
Parson, Oliver, University of Southampton
Prados, Emmanuel, INRIA
Provan, Gregory, University College Cork
Ramakrishnan, Naren, Virginia Tech
Ramchurn, Sarvapali, University of Southampton
Reid, Alistair, NICTA
Robu, Valentin, Heriot-Watt University
Sabbadin, Regis, MIAT INRA
Sabharwal, Ashish, Allen Institute for AI
Schönfelder, René, Universität zu Lübeck
Schumann, Anika, IBM Research
Selman, Bart, Cornell University
Simonis, Helmut, University College Cork
Singla, Adish, ETH Zurich
Sinha, Arunesh, USC
Soundarajan, Sucheta, Rutgers Univ
Stein, Sebastian, University of Southampton
Steinhaeuser, Karsten, University of Minnesota
Subramanian, Lakshminarayanan, New York University
Thiebaux, Sylvie., ANU
Tucker, Allan, Brunel University
Urieli, Daniel, The University of Texas at Austin
Wong, Weng-Keen, Oregon State University
Xue, Yexiang, Cornell University
Yang, Rong, Palo Alto Research Center