Lauren is the program manager for Earth Analytics Education at Earth Lab. She manages the Earth Data Analytics - Foundations Professional Certificate, oversees the Earth Lab internship program, and researches data-intensive education. She loves working at Earth Lab alongside colleagues who share an excitement for connecting science with people and real world problems.

Lauren brings seven years of experience working at the intersection of earth science and education to Earth Lab’s education team. Most recently, she worked as a GIS & Remote Sensing Specialist at a water conservation startup. Prior to that, she pursued a Master’s degree in geography funded by a U.S. FLAS Fellowship, researched water management in Tajikistan with a U.S. Fulbright grant, and worked in geosciences policy at the American Geosciences Institute. She holds a B.S. in Geosciences and B.A. in Geography from Penn State University and a M.A. in Geography from the University of Arizona.

In her free time, she enjoys serving on the Board of Directors of a local self-sufficiency program for homeless women and spending time outside.

Our curriculum teaches skills at the intersection of environmental science and data science, combined with a knowledge of heterogeneous data types, open science approaches, and a suite of “soft” skills needed to communicate and collaborate in interdisciplinary teams. These in-demand skills prepare students and professionals for careers in data intensive science that address a variety of large scale environmental challenges.
Education should be open and available to anyone. The earthdatascience.org learning portal has over 300 free and open lessons that allow anyone to learn and teach earth data science skills and has tens of thousands of visitors each month.
Earth Lab’s evaluation efforts contribute to the larger field of data science education by identifying best practices surrounding earth data science education in online and in person environments and across diverse demographics.
Our innovative classrooms include an interdisciplinary mix of undergraduate, graduate, and returning professional students who participate in active, collaborative synchronous and asynchronous learning through options for in-person or online courses. This variety in available participation modes supports students by addressing geographic and time constraints that might otherwise limit their access to formal education in earth data science.