Tuesday, October 10, 2023 11am to 12pm
About this Event
I study statistical and geospatial machine learning. My research blends methodological and applied techniques to study and design machine learning algorithms with an emphasis on usability, data-efficiency and fairness.
Current 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 looking for students interested in statistical or geospatial machine learning methodology and tailoring those methods to the context of real world problems. Prospective students should be excited about working in and actively fostering a collaborative, supportive, and communicative research community in our lab doing exciting, innovative, and sometimes interdisciplinary research. Ideal background includes skills in statistics, optimization, linear algebra and python.
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