Esther Rolf Personal Website

I am an assistant professor of computer science at the Univeristy of Colorado, Boulder.

I research statistical and geospatial machine learning. My research blends methodological and applied techniques to study and design machine learning algorithms and systems with an emphasis on usability, data-efficiency and fairness. My current research directions include developing algorithms and infrastructure for reliable environmental monitoring using machine learning, and understanding the multifaceted nature of representation in data and how that affects our ability to train fair and effective machine learning systems.

I'm hiring! See below for information on joining my lab at CU Boulder!

Research

In my research I develop and analyze of machine learning algorithms to address pressing social and environmental problems. Sometimes this entails developing or analyzing new statistical ML techniques, sometimes this entails carefully applying, adapting, and evaluating ML methods in a specific context of use; most often it entails a blend of the two. Some specific project areas and application contexts:

Tailored machine learning for remotely sensed data (e.g. using satellite imagery for environmental monitoring).
Characterizing and formalizing notions of representivity in training data (e.g. numerical representation: how many data points come from each source or group?, as distinct from what components of an individual or environment does a collection of data actually reflect?), and how these notions of representation affect our ability to train fair and useful machine learning systems.
Understanding and addressing key challenges in geospatial machine learning (e.g. spatial error structures make it hard to evaluate geospatial ML models, and can introduce concerns of bias or unfairness in downstream use).

For a full list of papers please see my google scholar page.

Selected Projects

Spotlight presentation! "Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning" (w/ Hannah Kerner, Konstantin Klemmer, and Caleb Robinson) will appear at ICML 2024.

New! "Application-Driven Innovation in Machine Learning " (w/ David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Milind Tambe, and Adam Whiten) will appear at ICML 2024.

"Geographic location encoding with spherical harmonics and sinusoidal representation networks" (w/ Marc Rußwurm, Konstantin Klemmer, Robin Zbinden, and Devis Tuia) appeared at ICLR 2024.

"Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy" (w/ Emily Aiken and Joshua Blumenstock) appeared at IJCAI 2023.

Figure showing key distinctions of geospatial ML from other learning paradigms: data are often autocorrelated and only available in certain locations, while prediction models are often expected to geographically extrapolate (diagram showing this: train in one region, use in another region ).

Evaluation Challenges for Geospatial ML

Esther Rolf. Workshop on Machine Learning for Remote Sensing at ICLR, 2023.

Figure showing that using implicit posterior models, we can resolve coarse landcover labels into high-resolution landcover predictions, by pairing uncertain prior beliefs based on avilable landcover data with high resolution satellite image observations.

Resolving label uncertainty with implicit posterior models

Esther Rolf*, Nikolay Malkin*, Alexandros Graikos, Ana Jojic, Caleb Robinson, Nebojsa Jojic. UAI, 2022.

Oral presentation (5% of accepted papers)

$Figure illustrating that by parametrizing the training dataset in terms of the fraction of training samples from each of many population groups, we can calculate the optimal training distribution for learning procedures under different conditions. Doing so formalizes the important of sub-group representation of small groups in training data, even when the objective is population average accuracy.$

Representation matters: Assessing the importance of subgroup allocations in training data

Esther Rolf, Theodora Worledge, Benjamin Recht, Michael I. Jordan. ICML, 2021.

A schematic of the MOSAIKS system for satellite imagery and machine learning. Satellite images are converted into a fixed embedding matrix X, which can be used to solve downstream prediction tasks with a simple linear regression model (shown here are predictions for forest cover and population density).

A generalizable and accessible approach to machine learning with global satellite imagery

Esther Rolf*, Jonathan Proctor*, Tamma Carleton*, Ian Bolliger*, Vaishaal Shankar*, Miyabi Ishihara, Benjamin Recht, Solomon Hsiang. Nature Communications, 2021.

How to join

I am currently recruiting postdocs and PhD Students to join my lab at CU, who are interested in

statistical and/or geospatial ML methodology, and
context-driven research for real-world problems

and who want to work in and foster a lab environment which is

doing innovative, sometimes interdisciplinary research
collaborative, supportive, and communication-forward.

Interested in a PhD, postdoc, or other involvment with the lab? Please first read my guide to getting involved with the lab, which includes instructions for the best way to contact me. Please note that I will not be able to respond to all emails.

Courses

Current Topics in Computer Science: Geospatial and Statistical Machine Learning

Fall 2024 Tues/Thurs 12:30-1:45pm, CU Boulder (CSCI 7000)

Course description and learning objectives

Resources

PhD application resources

CU Boulder's graduate admissions homepage

CU Boulder's tips for creating an impactful application and opportunities for an application fee waiver.

MOSAIKS: Generalizable and accessible machine learning with global satellite imagery

The MOSAIKS data interface has moved to api.mosaiks.org!

See the MOSAIKS project page for more resources and updates.