Item talk:Q160040
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orcid:
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meta: url: https://www.usgs.gov/staff-profiles/gericke-cook timestamp: '2024-01-30T22:19:14.805086' status_code: 200 profile: name: Gericke Cook name_qualifier: null titles: - Supervisory Data Scientist organizations: - !!python/tuple - Water Resources Mission Area - https://www.usgs.gov/mission-areas/water-resources email: gcook@usgs.gov orcid: 0000-0003-2741-787X intro_statements: - Gericke is a Supervisory Data Scientist in the Data Science Branch for the USGS Water Resources Mission Area. expertise_terms: - Data Science - Ecology - Machine Learning - R - GIS - Risk Assessment - Agriculture - Invasive Species professional_experience: - "Jan 2017 \u2013 Nov 2022. Mathematical Statistician: Transboundary Disease\ \ Analytics team. USDA APHIS, Veterinary Services, Center for Epidemiology and\ \ Animal Health" - 'Jan-Apr 2020. Interdisciplinary Supervisor (detail): Risk Assessment team. USDA APHIS, Veterinary Services, Center for Epidemiology and Animal Health' - 'Jun-Sep 2019. Interdisciplinary Supervisor (detail): Risk Identification team. USDA APHIS, Veterinary Services, Center for Epidemiology and Animal Health' - "Oct 2012 \u2013 Dec 2016 Quantitative Analyst. USDA APHIS, Plant Protection\ \ and Quarantine, Center for Plant Health Science and Technology" - "Feb 2009 \u2013 Sep 2012 Regional GIS Specialist. USDA APHIS, Plant Protection\ \ and Quarantine, Field Operations." - "Jan 2008 \u2013 Jan 2009. Cartographic Technician. USDA APHIS, Plant Protection\ \ and Quarantine, Field Operations." education: - Certificate, Geostatistics and Open-Source Statistical Computing, 2010. University of Twente, Enschede, Netherlands. - PhD, Ecology (unawarded). Colorado State University. Fort Collins, Colorado. - BS, Mathematics and Biology. 1998. Western Kentucky University. Bowling Green, KY. affiliations: [] honors: [] abstracts: [] personal_statement: 'Gericke Cook is the lead for applied data science in the Data Science Branch of the Integrated Information Dissemination Division. She leads ingestion and integration of difficult data into meaningful analyses and predictions to support decision making for Water Mission Area: state-of-the-art AI/ML predictions, forecasts, and projections; engineering the pipeline architecture for processing, deployment, and operationalization of those models in HPC or cloud environments; and delivering analytic reporting on how our data and models are being consumed by the public (prep, predict, report).'
