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 effect 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

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

How to join!

I am looking for postdocs and PhD Students to join my lab at CU, starting as soon as the fall of 2024, 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? Apply to CU Boulder's PhD program in computer science and list me as a potential advisor. Applications open in the fall for positions in fall of the following year.

Interested in a postdoc? Send me an email at describing your background, research interests, and fit with my lab.

Other? Email me at Please note that I will not be able to respond to all emails.


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 API (data interface) is now available: go to to make an account and use precomputed global features (using satellite imagery from 2019).
  • See the MOSAIKS project page for more resources and updates.