Nicole Taverna is a member of the Data Analytics, Tools, and Applications Group within the Strategic Energy Analysis Center. She has been heavily involved in curation for the Geothermal Data Repository since she was hired in 2017 and has since expanded her expertise to include machine learning, interactive data visualization, data pipeline development, and statistical methods and analyses. Her experience lies primarily in geothermal energy, but she has worked on projects pertaining to marine energy and water innovation as well.

Research Interests

Data-centric machine learning

Statistical methods for data analysis

Geophysics

Education

M.S. in Data Science, Colorado School of Mines, 2023 (in progress)

B.S. in Geophysics with a minor in Computer Science, Colorado School of Mines, 2019

Associations and Memberships

Membership Database Manager, Women in Geothermal

Featured Work

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning, Stanford Geothermal Workshop (2022)

Distributed Sensing and Machine Learning Hone Seismic Listening, Eos (2022)

A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML), Energies (2021)

PoroTomo DAS Data Processing Tutorials, Registry of Open Data on AWS (2021)

The Geothermal Data Repository: Five Years of Open Geothermal Data, Benefits to the Community, GRC Transactions (2017)

Awards and Honors

ARCS Scholar Award (2016, 2017)

SEG/Denver Geophysical Society Scholar (2016, 2018)

Shirley A. and Stanley H. Ward Scholar (2017, 2018)