Document Type : Analytical note
Author
University of Tehran
Abstract
These are exciting times for computational sciences with the digital revolution permeating a variety of areas and radically transforming business, science, and our daily lives. The Internet and the World Wide Web, GPS, satellite communications, remote sensing, and smartphones are dramatically accelerating the pace of discovery, engendering globally connected networks of people and devices. The rise of practically relevant artificial intelligence (AI) is also playing an increasing part in this revolution, fostering e-commerce, social networks, personalized medicine, IBM Watson and AlphaGo, self-driving cars, and other groundbreaking transformations.
Key Insights
Computer science enriches sustainability. Computer scientists can and should make important contributions to help address key societal and environmental challenges facing humanity, in pursuit of a sustainable future. The new field of computational sustainability brings these efforts together.
Sustainability enriches computer In turn, working on sustainability problems, which involve uncertainty, machine learning, optimization, remote sensing, and decision making, enriches computer science by generating compelling new computational problems.
Sustainability concerns human well-being and the protection of the planet. A large group of computer science researchers, collaborating with an even larger group of domain from social, environmental, and natural sciences, can drive computational sustainability in ways that would not be possible in a smaller or less interdisciplinary setting.
Keywords
Main Subjects
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https://proceedings.mlr.press/v84/grover18b.html
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https://doi.org/10.1126/science.aaf7894
Kelling S, Johnston A, Hochachka WM, Iliff M, Fink D, & Gerbracht J. (2015) Can Observation Skills of Citizen Scientists Be Estimated Using Species Accumulation Curves?. PLoS ONE, 10(10), e0139600.
https://doi.org/10.1371/journal.pone.0139600
Khazaei, J., & Powell, W. B. (2018). SMART-Invest: A stochastic, dynamic planning for optimizing investments in wind, solar, and storage in the presence of fossil fuels. Energy Systems, 9(2), 277–303.
https://doi.org/10.1007/s12667-016-0226-4
Kraus, S. (2001). Automated negotiation and decision-making in multiagent environments. In ECCAI Advanced Course on Artificial Intelligence. Springer. United States.
Lässig, J., Kersting, K., & Morik, K. (Eds.). (2016). Computational Sustainability (Vol. 645). Springer. United States.
Molina, S., Fuller, A. K., Morin, D. J., & Royle, J. A. (2017). Use of spatial capture–recapture to estimate density of Andean bears in northern Ecuador. Ursus, 28(1), 117-126.
https://doi.org/10.2192/URSU-D-16-00030.1
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https://doi.org/10.1016/j.ecolmodel.2005.03.026
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https://doi.org/10.1016/j.ejor.2018.07.014
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https://dl.acm.org/doi/10.5555/2986459.2986589
Sheldon, D., Farnsworth, A., Irvine, J., Van Doren, B., Webb, K., Dietterich, T., & Kelling, S. (2013). Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar. Proceedings of the AAAI Conference on Artificial Intelligence. 27(1), 1334-1340. California, USA.
https://doi.org/10.1609/aaai.v27i1.8486
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Http://Www.Undatarevolution.Org/Wp-Content/Uploads/2014/12/A-World-That-Counts2.Pdf
Wahabzada, M., Mahlein, A. K., Bauckhage, C., Steiner, U., Oerke, E. C., & Kersting, K. (2016). Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants. Scientific reports, 6(1), 22482.
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Wu, X., Gomes-Selman, J., Shi, Q., Xue, Y., Garcia-Villacorta, R., Anderson, E., Sethi, S., Steinschneider, S., Flecker, A., & Gomes, C. (2018). Efficiently Approximating the Pareto Frontier: Hydropower Dam Placement in the Amazon Basin. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).
https://doi.org/10.1609/aaai.v32i1.11347
Xue, Y., Davies, I., Fink, D., Wood, C., & Gomes, C. P. (2016). Avicaching: A two-stage game for bias reduction in citizen science. Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems. Singapore. 776–785.
https://dl.acm.org/doi/10.5555/2936924.2937038
Yadav, A., & et al. (2017). Influence maximization in the field: The arduous journey from emerging to deployed application. Proceedings of the 16th Conference on Autonomous Agents and Multiagent Systems, Sao Paulo, Brazil. 150–158.
https://dl.acm.org/doi/10.5555/3091125.3091152
https://doi.org/10.1109/ECC.2016.7810598
Albers, J. H., Dietterich, T., Hall, K., Lee, K., & Taleghan, M. (2018). Simulator-defined Markov decision processes: A case study in managing bio-invasions. In F. Fang, M. Tambe, B. Dilkina, & A. Plumptre (Eds.), Artificial Intelligence and Conservation. Cambridge University Press. Cambridge, United Kingdom.
Azimi, J., Fern, X., & Fern, A. (2016). Budgeted optimization with constrained experiments. Journal of Artificial Intelligence Research, 56, 119–152.
https://doi.org/10.1613/jair.4896
Bai, J., Xue, Y., Bjorck, J., Le Bras, R., Rappazzo, B., Bernstein, R., Suram, S. K., van Dover, R. B., Gregoire, J. M., & Gomes, C. P. (2018). Phase mapper: Accelerating materials discovery with AI. AI Magazine, 39(1), 15–26.
https://doi.org/10.1609/aimag.v39i1.2785
Barrett, C., Garg, T., & McBride, L. (2016). Well-being dynamics and poverty traps. Annual Review of Resource Economics, 8, 303–327.
https://doi.org/10.1146/annurev-resource-100815-095235
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https://proceedings.mlr.press/v70/bernstein17a.html
Chen, D., Xue, Y., & Gomes, C. (2018). End-to-end learning for the deep multivariate probit model. Proceedings of the International Conference on Machine Learning (ICML). Stockholm, Sweden.
https://proceedings.mlr.press/v80/chen18o.html
Coble, K., Mishra, A., Ferrell, S., & Griffin, T. (2018). Big data in agriculture: A challenge for the future. Applied Economic Perspectives and Policy, 40(1), 79–96.
https://doi.org/10.1093/aepp/ppx056
Dilkina, B., Houtman, R., Gomes, C. P., Montgomery, C. A., McKelvey, K. S., Kendall, K., & Schwartz, M. K. (2017). Trade-offs and efficiencies in optimal budget-constrained multi-species corridor networks. Conservation Biology, 31(1), 192–202.
https://doi.org/10.1111/cobi.12814
Donti, P., Kolter, J. Z., & Amos, B. (2017). Task-based end-to-end model learning in stochastic optimization. Advances in Neural Information Processing Systems, 5490–5500.
https://doi.org/10.48550/arXiv.1703.04529
Ermon, S., Xue, Y., Toth, R., Dilkina, B., Bernstein, R., Damoulas, T., Clark, P., DeGloria, S., Mude, A., Barrett, C., & Gomes, C. (2015). Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).
https://doi.org/10.1609/aaai.v29i1.9248
Faghmous, J., & Kumar, V. (2014). A big data guide to understanding climate change: The case for theory-guided data science. Big Data, 2(3), 155–163.
https://doi.org/10.1089/big.2014.0026
Fang, F., Nguyen, T. H., Pickles, R., Lam, W. Y., Clements, G. R., An, B., Singh, A., Schwedock, B. C., Tambe, M., & Lemieux, A. (2017). PAWS — A Deployed Game-Theoretic Application to Combat Poaching. AI Magazine, 38(1), 23-36.
https://doi.org/10.1609/aimag.v38i1.2710
Fang, F., Tambe, M., Dilkina, B., & Plumptre, A. (Eds.). (2018). Artificial Intelligence and Conservation. Cambridge University Press. Cambridge, United Kingdom.
Fink, D., Hochachka, W. M., Zuckerberg, B., Winkler, D. W., Shaby, B., Munson, M. A., & Kelling, S. (2010). Spatiotemporal exploratory models for broad-scale survey data. Ecological Applications, 20(8), 2131-2147.
https://doi.org/10.1890/09-1340.1
Fisher, D. H. (2016). Recent advances in AI for computational sustainability. IEEE Intelligent Systems, 31(4), 56–61.
https://doi.org/10.1109/MIS.2016.61
Freund, D., Henderson, S. G., & Shmoys, D. B. (2018). Sharing Economy: Making Supply Meet Demand. Springer. United States.
Giesen, N., Hut, R., & Selker, J. (2014). The Trans-African Hydro-Meteorological Observatory (TAHMO). Wiley Interdisciplinary Reviews: Water, 1(4), 341–348.
https://doi.org/10.1002/wat2.1034
Gomes, C. P. (2009). Computational sustainability: Computational methods for a sustainable environment, economy, and society. The Bridge, 39(4), 5–13.
https://nap.nationalacademies.org/read/12821/chapter/7#28
Grover, A., Markov, T., Attia, P., Jin, N., Perkins, N., Cheong, B., & Ermon, S. (2018, March). Best arm identification in multi-armed bandits with delayed feedback. In International conference on artificial intelligence and statistics (833-842). PMLR. New York, USA.
https://proceedings.mlr.press/v84/grover18b.html
Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790–794.
https://doi.org/10.1126/science.aaf7894
Kelling S, Johnston A, Hochachka WM, Iliff M, Fink D, & Gerbracht J. (2015) Can Observation Skills of Citizen Scientists Be Estimated Using Species Accumulation Curves?. PLoS ONE, 10(10), e0139600.
https://doi.org/10.1371/journal.pone.0139600
Khazaei, J., & Powell, W. B. (2018). SMART-Invest: A stochastic, dynamic planning for optimizing investments in wind, solar, and storage in the presence of fossil fuels. Energy Systems, 9(2), 277–303.
https://doi.org/10.1007/s12667-016-0226-4
Kraus, S. (2001). Automated negotiation and decision-making in multiagent environments. In ECCAI Advanced Course on Artificial Intelligence. Springer. United States.
Lässig, J., Kersting, K., & Morik, K. (Eds.). (2016). Computational Sustainability (Vol. 645). Springer. United States.
Molina, S., Fuller, A. K., Morin, D. J., & Royle, J. A. (2017). Use of spatial capture–recapture to estimate density of Andean bears in northern Ecuador. Ursus, 28(1), 117-126.
https://doi.org/10.2192/URSU-D-16-00030.1
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3–4), 231–259.
https://doi.org/10.1016/j.ecolmodel.2005.03.026
Powell, W. (2019). A unified framework for stochastic optimization. European Journal of Operational Research, 275(3), 795–821.
https://doi.org/10.1016/j.ejor.2018.07.014
Reynolds, M. D., & et al. (2017). Dynamic conservation for migratory species. Science Advances, 3(8), e1700707.
https://doi.org/10.1126/sciadv.1700707
Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin III, F. S., Lambin, E., & Foley, J. (2009). Planetary boundaries: Exploring the safe operating space for humanity. Ecology and Society, 14(2), Article 32.
https://doi.org/10.1126/sciadv.1700707
Rudin, C., & Wagstaff, K. (2014). Machine learning for science and society. Machine Learning, 95(1), 1–9.
https://doi.org/10.1007/s10994-013-5425-9
Ruiz-Muñoz, J. F., You, Z., Raich, R., & Fern, X. Z. (2018). Dictionary learning for bioacoustics monitoring with applications to species classification. Journal of Signal Processing Systems, 90(2), 233–247.
https://doi.org/10.1007/s11265-016-1155-0
Russell, S., Dietterich, T., Horvitz, E., Selman, B., Rossi, F., Hassabis, D., Legg, S., Suleyman, M., George, D., & Phoenix, S. (2015). Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter. AI Magazine, 36(4), 3-4.
https://doi.org/10.1609/aimag.v36i4.2621
Sheldon, D. R., & Dietterich, T. G. (2011). Collective graphical models. Proceedings of the 25th International Conference on Neural Information Processing Systems, 1161–1169. Granada, Spain.
https://dl.acm.org/doi/10.5555/2986459.2986589
Sheldon, D., Farnsworth, A., Irvine, J., Van Doren, B., Webb, K., Dietterich, T., & Kelling, S. (2013). Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar. Proceedings of the AAAI Conference on Artificial Intelligence. 27(1), 1334-1340. California, USA.
https://doi.org/10.1609/aaai.v27i1.8486
Sullivan, B. L., Aycrigg, J. L., Barry, J. H., Bonney, R. E., Bruns, N., Cooper, C. B., & Kelling, S. (2014). The eBird enterprise: An integrated approach to development and application of citizen science. Biological Conservation, 169, 31–40.
https://doi.org/10.1016/j.biocon.2013.11.003
Tambe, M., & Rice, E. (Eds.). (2018). Artificial Intelligence and Social Work. Cambridge University Press. Cambridge, United Kingdom.
United Nations. (2018a). Our Common Future. Retrieved Aug. 25; http://www.un-documents.net/our-common-future.pdf
United Nations. (2018b). Transforming Our World: The 2030 Agenda for Sustainable Development. Retrieved Aug. 25; http://www.un.org/ga/search/view_doc.asp?symbol=a/res/70/&lang=e
United Nations. (2018c). A World That Counts: Mobilizing The Data Revolution for Sustainable Development. Retrieved June 16;
Http://Www.Undatarevolution.Org/Wp-Content/Uploads/2014/12/A-World-That-Counts2.Pdf
Wahabzada, M., Mahlein, A. K., Bauckhage, C., Steiner, U., Oerke, E. C., & Kersting, K. (2016). Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants. Scientific reports, 6(1), 22482.
https://doi.org/10.1038/srep22482
Wu, X., Gomes-Selman, J., Shi, Q., Xue, Y., Garcia-Villacorta, R., Anderson, E., Sethi, S., Steinschneider, S., Flecker, A., & Gomes, C. (2018). Efficiently Approximating the Pareto Frontier: Hydropower Dam Placement in the Amazon Basin. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).
https://doi.org/10.1609/aaai.v32i1.11347
Xue, Y., Davies, I., Fink, D., Wood, C., & Gomes, C. P. (2016). Avicaching: A two-stage game for bias reduction in citizen science. Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems. Singapore. 776–785.
https://dl.acm.org/doi/10.5555/2936924.2937038
Yadav, A., & et al. (2017). Influence maximization in the field: The arduous journey from emerging to deployed application. Proceedings of the 16th Conference on Autonomous Agents and Multiagent Systems, Sao Paulo, Brazil. 150–158.
https://dl.acm.org/doi/10.5555/3091125.3091152
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