Emma is interested in exploring how we can manage natural resources in a way that co-optimizes health and environmental outcomes in the context of global change. She aims to use tools from machine learning, econometrics, and epidemiology to evaluate and inform environmental policy and public health interventions. She is a NSF Graduate Research Fellow, a Stanford EDGE Fellow, and a Stanford Data Science Scholar.
Prior to starting her PhD, Emma worked as a Research Analyst at the Global Policy Lab for three years. During her time at GPL, she was part of a project that aimed to identify land-based sources of nonpoint source water pollution in national-scale river systems in New Zealand and the US Mississippi River Basin. Emma completed her MPH in global and environmental health at Columbia University and received a BA in neuroscience from Colgate University. When she isn’t at her desk, you can find her outside - most likely running or hiking up a mountain.
Honors & Awards
Stanford Data Science Scholar, Stanford Data Science (2023 - 2025)
Graduate Research Fellow, National Science Foundation (2022 - 2027)
EDGE Fellow, Stanford University (2022 - 2025)
Education & Certifications
BA, Colgate University, Behavioral Neuroscience (2015)
MPH, Columbia University, Environmental Health Sciences, Global Health Certificate (2017)
running, trivia, hiking, kayaking, crossword puzzle solving + constructing
Harmonized nitrogen and phosphorus concentrations in the Mississippi/Atchafalaya River Basin from 1980 to 2018
2022; 9 (1): 524
Water quality monitoring can inform policies that address pollution; however, inconsistent measurement and reporting practices render many observations incomparable across bodies of water, thereby impeding efforts to characterize spatial patterns and long-term trends in pollution. Here, we harmonized 9.2 million publicly available monitor readings from 226 distinct water monitoring authorities spanning the entirety of the Mississippi/Atchafalaya River Basin (MARB) in the United States. We created the Standardized Nitrogen and Phosphorus Dataset (SNAPD), a novel dataset of 4.8 million standardized observations for nitrogen- and phosphorus-containing compounds from 107 thousand sites during 1980-2018. To the best of our knowledge, this dataset represents the largest record of these pollutants in a single river network where measurements can be compared across time and space. We addressed numerous well-documented issues associated with the reporting and interpretation of these water quality data, heretofore unaddressed at this scale, and our approach to water quality data processing can be applied to other nutrient compounds and regions.
View details for DOI 10.1038/s41597-022-01650-6
View details for Web of Science ID 000846233000007
View details for PubMedID 36030259
View details for PubMedCentralID PMC9420138
- The effect of large-scale anti-contagion policies on the COVID-19 pandemic (vol 584, pg 262, 2020) NATURE 2020; 585 (7824): E7