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Machine Learning for Sustainability
Edwin Bonilla · Thomas Dietterich · Theodoros Damoulas · Andreas Krause · Daniel Sheldon · Iadine Chades · J. Zico Kolter · Bistra Dilkina · Carla Gomes · Hugo P Simao

Tue Dec 10 07:30 AM -- 06:30 PM (PST) @ Harrah's Glenbrook+Emerald
Event URL: https://sites.google.com/site/mlsustws/ »
Sustainability encompasses the balance of environmental, economic and societal demands. There is strong evidence suggesting that more actions need to be taken in order to achieve this balance. For example, Edward O. Wilson said in his 2002 Book The Future of Life that "at the current rates of human destruction of natural ecosystems, 50% of all species of life on earth will be extinct in 100 years". More recently, a 2012 review in Nature has stated that, similarly to localized ecological systems, "the global ecosystem as a whole can react in the same way and is approaching a planetary-scale critical transition as a result of human influence".

While the significance of the problem is apparent, more involvement from the machine learning community in sustainability problems is required. Not surprisingly, sustainability problems bring along interesting challenges and opportunities for machine learning in terms of complexity, scalability and impact in areas such as prediction, modeling and control. This workshop aims at bringing together scientists in machine learning, operations research, applied mathematics and statistics with a strong interest in sustainability to discuss how to use existing techniques and how to develop novel methods in order to address such challenges.

There are many application areas in sustainability where machine learning can have a significant impact. For example:

- Climate change
- Conservation and biodiversity
- Socio-economic systems
- Understanding energy consumption
- Renewable energy
- Impact of mining
- Sustainability in the developing world
- Managing the power grid
- Biofuels

Similarly, machine learning approaches to sustainability problems can be drawn from several fields such as:

- Constraint optimization
- Dynamical systems
- Spatio-temporal modeling
- Probabilistic inference
- Sensing and monitoring
- Decision making under uncertainty
- Stochastic optimization

The talks and posters are expected to span (but not be limited to) the above areas. More importantly, there will be a specific focus on how cutting-edge machine learning research is developed (i.e. not only using off-the-shelf ML techniques) in order to address challenges in terms of complexity, scalability and impact that sustainability problems may pose.

The main expected outcomes of this workshop are: (1) attracting more people to work on computational sustainability; (2) transfer of knowledge across different application domains; and (3) emerging collaboration between participants. More long-term avenues such as datasets and competitions will be explored.

There will be an award (~ $$250 book voucher) for the best contribution, which will be given an oral presentation.

Author Information

Edwin Bonilla (Data61)
Thomas Dietterich (Oregon State University)

Tom Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor and Director of Intelligent Systems Research at Oregon State University. Among his contributions to machine learning research are (a) the formalization of the multiple-instance problem, (b) the development of the error-correcting output coding method for multi-class prediction, (c) methods for ensemble learning, (d) the development of the MAXQ framework for hierarchical reinforcement learning, and (e) the application of gradient tree boosting to problems of structured prediction and latent variable models. Dietterich has pursued application-driven fundamental research in many areas including drug discovery, computer vision, computational sustainability, and intelligent user interfaces. Dietterich has served the machine learning community in a variety of roles including Executive Editor of the Machine Learning journal, co-founder of the Journal of Machine Learning Research, editor of the MIT Press Book Series on Adaptive Computation and Machine Learning, and editor of the Morgan-Claypool Synthesis series on Artificial Intelligence and Machine Learning. He was Program Co-Chair of AAAI-1990, Program Chair of NIPS-2000, and General Chair of NIPS-2001. He was first President of the International Machine Learning Society (the parent organization of ICML) and served a term on the NIPS Board of Trustees and the Council of AAAI.

Theodoros Damoulas (New York University)
Andreas Krause (ETHZ)
Daniel Sheldon (University of Massachusetts Amherst)
Iadine Chades (CSIRO)
J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

Zico Kolter is an Assistant Professor in the School of Computer Science at Carnegie Mellon University, and also serves as Chief Scientist of AI Research for the Bosch Center for Artificial Intelligence. His work focuses on the intersection of machine learning and optimization, with a large focus on developing more robust, explainable, and rigorous methods in deep learning. In addition, he has worked on a number of application areas, highlighted by work on sustainability and smart energy systems. He is the recipient of the DARPA Young Faculty Award, and best paper awards at KDD, IJCAI, and PESGM.

Bistra Dilkina (Cornell University)
Carla Gomes (Cornell University)
Hugo P Simao (Princeton University)

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