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Galen Gorski

Galen Gorski is a Machine Learning Specialist for the USGS Water Mission Area, based in Reston, VA. 

I am interested in understanding fundamental connections between hydrologic and biogeochemical cycling in natural and managed landscapes. This requires a suite of methods and approaches including data-driven techniques, field campaigns, and laboratory analysis. I am interested in projects that use machine learning and other data-driven techniques to cut across these boundaries and develop understanding of the connections between water quantity and quality and their implications for water resource management and sustainability.    

In my previous work, I investigated controls on nitrogen cycling in managed aquifer recharge settings in agricultural areas of California. I have also worked to develop local and regional recharge suitability maps in California and the Middle East North Africa Region to help guide future project siting. I’m excited about combining my experiences with the incredible expertise at USGS to tackle important water resource challenges. 

Professional Experience

  • ​2021- Present Machine Learning Specialist, U.S. Geological Survey, Water Mission Area

  • 2021 Postdoctoral Scholar, University of California, Berkeley, Mentor: Laurel Larsen 

  • 2020 Postdoctoral Researcher, University of California, Santa Cruz; Mentors: Andrew Fisher and Margaret Zimmer 

  • 2014-2015 Biological Technician, University of Minnesota/USDA 

  • ​2013-2014 Laboratory Technician

Education and Certifications

  • ​Ph.D. Earth Sciences, 2020, University of California, Santa Cruz; Advisors: Andrew Fisher and Adina Paytan
    Linking hydrologic and biogeochemical cycling across scales: Implications for nutrient and w

  • B.A. Chemistry, 2013, Carleton College
    Probing the primary events of photosynthesis using ultra-fast lasers

Science and Products

A deep learning model and associated data to support understanding and simulation of salinity dynamics in Delaware Bay

Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state-of-the-art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg/L Cl- isoc
By
Water Resources Mission Area

Non-USGS Publications**

Gorski G., Zimmer M.A. Hydrologic regimes drive nutrient export behavior in human
impacted watersheds. (2021) Hydrology and Earth System Science. 25, 1333-1345
doi: https://doi.org/10.5194/hess-25-1333-2021
Pensky J., Richardson C., Serrano A., Gorski G., Price A.N., Zimmer M.A., (2021)
Disrupt and demystify the unwritten rules of graduate school. Nature Geosciences 14,
538-539. doi: http://dx.doi.org/10.1038/s41561-021-00799-w
Gorski G., Dailey H., Fisher A.T., Schrad N., Saltikov C. (2020) Denitrification during
infiltration for managed aquifer recharge: Infiltration rate controls and microbial response.
Science of the Total Environment, 727, 138642.
doi: https://doi.org/10.1016/j.scitotenv.2020.138642
Gorski G., Fisher A.T., Beganskas S., Weir W., Redford K., Schmidt C., Saltikov C.
(2019) Field and laboratory studies linking hydrologic, geochemical, and microbiological
processes and enhanced denitrification during infiltration for managed recharge. Environ-
mental Science and Technology, 53, 9491-9501.
doi: http://dx.doi.org/10.1021/acs.est.9b01191
Gorski G., Strong C., Good S.P., Bares R., Ehleringer J.R., Bowen G.J. (2015) Vapor
hydrogen and oxygen isotopes reflect water of combustion in the urban atmosphere. Proceedings of the National Academy of Sciences, 112, 3247-3252.
doi: https://doi.org/10.1073/pnas.1424728112

**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.

Science and Products

A deep learning model and associated data to support understanding and simulation of salinity dynamics in Delaware Bay

Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state-of-the-art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg/L Cl- isoc
By
Water Resources Mission Area

Non-USGS Publications**

Gorski G., Zimmer M.A. Hydrologic regimes drive nutrient export behavior in human
impacted watersheds. (2021) Hydrology and Earth System Science. 25, 1333-1345
doi: https://doi.org/10.5194/hess-25-1333-2021
Pensky J., Richardson C., Serrano A., Gorski G., Price A.N., Zimmer M.A., (2021)
Disrupt and demystify the unwritten rules of graduate school. Nature Geosciences 14,
538-539. doi: http://dx.doi.org/10.1038/s41561-021-00799-w
Gorski G., Dailey H., Fisher A.T., Schrad N., Saltikov C. (2020) Denitrification during
infiltration for managed aquifer recharge: Infiltration rate controls and microbial response.
Science of the Total Environment, 727, 138642.
doi: https://doi.org/10.1016/j.scitotenv.2020.138642
Gorski G., Fisher A.T., Beganskas S., Weir W., Redford K., Schmidt C., Saltikov C.
(2019) Field and laboratory studies linking hydrologic, geochemical, and microbiological
processes and enhanced denitrification during infiltration for managed recharge. Environ-
mental Science and Technology, 53, 9491-9501.
doi: http://dx.doi.org/10.1021/acs.est.9b01191
Gorski G., Strong C., Good S.P., Bares R., Ehleringer J.R., Bowen G.J. (2015) Vapor
hydrogen and oxygen isotopes reflect water of combustion in the urban atmosphere. Proceedings of the National Academy of Sciences, 112, 3247-3252.
doi: https://doi.org/10.1073/pnas.1424728112

**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.