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Sustainability Data Science Conference Awards

We're absolutely thrilled to celebrate the recipients of the Best Presentation Awards at the Sustainability Data Science Conference! 

  • Anna Lukkarinen—Best Postdoc Presentation (top photo)
  • Mohammad Aljubran—Best Student Presentation (middle photo)
  • Andy Huynh—Best Student Presentation-2nd place (bottom photo)

Their passion for sustainability and data science is truly inspiring and we wholeheartedly appreciate their dedication and are eagerly anticipating their future accomplishments.

Anna's Presentation Title: Investor Reactions to Firm Sustainability—Evidence from a Randomized Field Experiment
Presentation Summary (Takeaways):
  • Question: How do investors react to firms’ sustainability cues?
  • Method: Randomized field experiment
  • Result: Reactions overall negative but vary by investor segment
  • Implications: Firms can mitigate negative reactions
Mohammad's Presentation Title: Thermal Earth Model for the Continental United States Using an Interpolative Physics-Informed Graph Neural Network (InterPIGNN)
Presentation Summary (Takeaways):
  • We aggregated a dataset with over 400,000 temperature-at-depth measurements and over 20 other physical quantities spanning the continental United States.
  • We developed a novel interpolative graph neural network (InterNet) for point cloud interpolation tasks, which we used to train a physics-informed model (InterPIGNN).
  • We achieved state-of-the-art performance in predicting temperature-at-depth with mean absolute error of 4.8 °C, ten folds less than the best model available in the literature.
Andy's Presentation Title: Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagery
Presentation Summary (Takeaways):
  • We leverage the relationship between species’ characteristics and their environment to build a new pre-training task for natural world imagery
  • CRISP multi-view pre-training improves performance on fine-grained visual classification tasks, especially rare species and undersampled regions
  • The benefit of our multi-view pre-training approach extends to ambient data collections available across the world

Photo credits: David Gonzales Photos