Machine Learning for Sustainability

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.