I am a NASA Postdoctoral Program (NPP) fellow affiliated with the NASA Earth Exchange and Biospheric Science Branch at Ames Research Center. My NPP project focuses on statistical methods for quantifying uncertainty in interpolated near-surface meteorological data products. Check out this preprint for more details. I also work on various theoretical and applied topics in remote sensing of agriculture, particularly topics related to irrigation. In this capacity, I contribute to OpenET. I have written many thousands of lines of Python but I am now a vociferous advocate for Julia.
I did a PhD in environmental engineering at Stanford University (graduated March 2023), where I was a member of the WE3 Lab. I was a Stanford Data Science Scholar in 2020-2021. I also have a MS in public policy and management from Carnegie Mellon and a BS in mathematics from Stanford. Between undergrad and grad school, I worked at SingleStore (fka MemSQL).
Contact: doherty.conor@gmail.com
Doherty, C.T., et al., (in revision). [preprint] "A Method for Quantifying Uncertainty in Spatially Interpolated Meteorological Data with Application to Daily Maximum Air Temperature."
Doherty, C.T., Wong, C.A., Mauter, M.S., (in preparation). "Satellite-based classification of irrigated fields without labeled training data."
Doherty, C.T., Mauter, M.S., (2025) [link]. "Fisher Discriminant Analysis for Extracting Interpretable Phenological Information from Multivariate Time Series Data." JSTARS.
Volk, J.M., [...], Doherty, C.T., [...], (2024) [link]. "Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management." Nat Water.
Doherty, C.T., (tutorial). [link] "The Uses and Limitations of the Discrete Fourier Transform for Analyzing Intra-Annual Environmental Time Series."
Doherty, C.T., et al., (2022) [link]. "Effects of meteorological and land surface modeling uncertainty on errors in winegrape ET calculated with SIMS." Irrig Sci.
Melton, F.S., Huntington, J., [...], Doherty, C.T., [...], (2021). [link] "OpenET: Filling a Critical Data Gap in Water Management for the Western United States." JAWRA.
Data Assimilation (CEE 261D) Winter 2023: Dynamic systems and state-space representation, Kalman Filter (KF), Ensemble and Compressed-State KF for large systems, optimal filter tuning (TA).
Imaging with Incomplete Information (CEE 362G/CME 262) Spring 2022: Bayesian approach to inverse problems with applications in hydrologic imaging (TA).
Inclusive Mentoring in Data Science (BIODS 360) Winter 2022: Course for graduate students who serve as mentors for undergraduates at 2- and 4-year colleges (TA and curriculum development).
Remote Sensing of Hydrology (CEE 260D/ESS 224) Spring 2021: Survey of principles and methods used in remote sensing of hydrology (TA and curriculum development).
Environmental Policy Analysis (CEE 275D) Fall 2019: Introduction to policy analysis for environmental engineering graduate students (TA).
Inclusive Mentoring in Data Science Program (2021-2022): Weekly mentorship session with students enrolled at colleges with limited opportunities for research and mentorship. Activities include homework help, resume/application review, discussions about careers and graduate school. Participated for two years including assisting with administration and curriculum development in second year. [program website]