Computer science tools offer powerful solutions to problems that physical scientists encounter. However, their potential remains untapped due to limited channels for knowledge sharing and a lack of visibility into the state of the art applications. The “Machine Learning for Polar Regions” workshop will serve as an opportunity to close the existing gaps between machine learning (ML) experts and polar scientists by identifying current obstacles and opportunities for cross-disciplinary collaboration. The ultimate goal of the workshop is to educate polar scientists and machine learning experts on each respective field and create a strategic roadmap to accelerate research through a coordinated, cross-disciplinary effort.
The Workshop will start with presentations from climate and machine learning experts on current trends in each field, together and separately. The talks will be followed by a summary and a discussion of current projects that are pioneering the use of machine learning over polar regions. Posters will be presented in the evening & participants will be able to submit their work using the registration link (no later than May 31st). The Steering committee will select the work that will be presented during the Workshop and will notify the participants by June 4th. Submissions that already include both the polar and machine learning aspects will be given priority for presentation and are strongly encouraged. However, papers and work focusing also on a single aspect of the polar or machine learning disciplines (i.e. Generative Adversarial Networks, Deep CNNs in the case of ML or sea ice, Greenland and Antarctica mass loss and contribution of polar regions to sea level rise) are encouraged and welcome. Afterwards, participants will be broken down into working groups that will focus on the three following questions:
What are the current mechanisms impeding or facilitating collaboration among disciplines?
What are the mechanisms leading to the success of cross-disciplinary work?
What are the current funding or collaborative opportunities for this area?
The outcome of the workshop will be a report containing a review of cutting edge applications, literature on machine learning and polar regions, current mechanisms that can support the success for cross-disciplinary collaboration, and funding opportunities for both the climate and machine learning realms.
Welcoming remarks: 9:30 AM-9:50 AM
Spotlight technical presentations (15 min ea.): 9:50 AM-10:50 AM
Break: 10:50 AM-11:15 AM
5-6-6-8 Poster lightning presentations (5 min ea.): 11:15 AM-12:30 PM
Lunch Break: 12:30 PM-1:30 PM
Breakout sessions: 1:30 PM-3:00 PM
Break: 3:00 PM-3:15 PM
Follow up discussion on breakout sessions: 3:15 PM-4:00 PM