Deep Learning Measurements to Model Ecosystems’ Response to Environmental Change

In many applications, Machine Learning (ML) predictions are used to make downstream decisions. Acting on ML predictions however can change the distribution of features that the ML model relies on for predictions. The implication is that such downstream decisions procedures implicitly expect the ML model to generalize outside of the observational distribution. Unfortunately, this is often not the case, and ML models tend to be brittle outside of their training distribution. ML models will thus produce unreliable extrapolations, leading to poor downstream decisions based on wrong predictions. We will develop causal models for field hedge predictions that generalize to Canada from data in other countries, to study hedges’ impact on carbon sequestration, crop yield, and resilience. The end goal is to help address challenges related to climate change.

Faculty Supervisor:

Mathias Lécuyer

Student:

Partner:

École Polytechnique

Discipline:

Computer science

Sector:

Education

University:

The University of British Columbia

Program: