Environmental Internship Program - 2023 Booklet

Benjamin Liu ’24
COMPUTER SCIENCE
PROJECT TITLE
Beyond Roughness:
Statistical
Characterization of
Two-dimensional Fields
Under Random Sampling
ORGANIZATION(S)
Simons Research
Group, Department of
Geosciences, Princeton
University
LOCATION(S)
Princeton, New Jersey
MENTOR(S)
Frederik Simons,
Professor of Geosciences
I explored machine learning methods to estimate
and analyze different types of environmental
data. Environmental data come as geographically
distributed sets of measured or modeled
variables, for example rainfall or vegetation
type, that we may treat as samples of a spatial
random field and its temporal evolution. Random
fields are characterized by hyperparameters
that define the statistical relationship between
their values at different points in space and
time. The isotropic Matérn random field is a
general class with three continuous parameters
that define its spectral structure: variance
(σ2), mean-squared differentiability (ν) and
correlation length (ρ). Working with Frederik
Simons, I developed machine-learning methods
to estimate the parameters of random fields.
I employed MATLAB and Python programs
to generate large sets of training data and
developed a convolutional neural network to
estimate their parameters. I gained significant
experience working with neural networks and
non-classification forms of machine learning
methods. The project has given me more
insight into the research process in the field of
machine learning and artificial intelligence as
a whole and how to apply these technologies to
environmental data.
CLIMATE AND
ENVIRONMENTAL SCIENCE
27