معرفی
Esther Rolf is an Assistant Professor in the Department of Computer Science at the University of Colorado Boulder. Her research focuses on blending methodological and applied machine learning techniques to address social and environmental challenges, emphasizing usability, data-efficiency, and fairness. Her work includes developing algorithms for environmental monitoring with satellite imagery and advancing geospatial ML systems.
Her research explores the multifaceted role of data representation in machine learning, particularly how spatial distribution and data acquisition impact model fairness and efficacy. Projects include formalizing representivity in training data and tackling evaluation challenges in geospatial ML applications.
Recent publications highlight her expertise in satellite-based poverty mapping, multimodal data efficiency, and geospatial foundation models. She integrates specialized architectures and domain-specific benchmarks into her work.
- Best Paper Award, ICML (2018)
- Best Paper Award, NeurIPS Workshop on AI for Social Good (2019)
- NSF Graduate Research Fellowship
- Google Research Fellowship
Esther's lab at CU Boulder recruits PhD students and postdocs interested in statistical/geospatial ML and context-driven research for real-world problems. She teaches graduate courses in machine learning and geospatial ML at CU Boulder.
