Emma holds a MPH in Environmental Health with a certificate in Global Health from Columbia University, and a BA in Behavioral Neuroscience from Colgate University. Prior to starting her PhD, she worked as a research analyst at the Global Policy Lab at UC Berkeley where she modeled nonpoint source water pollution at a high spatial resolution. During her MPH, she spent six months working as a WASH Fellow in Malawi, where she contributed to data-driven programming to promote food security during a national state of emergency. This was precipitated by her time at the Earth Institute at Columbia University, during which she researched how factors such as agricultural intensification, demographics, and market-demand influenced crop yield in rural Africa.

Currently, she is interested in quantifying the causal relationship between large-scale, anthropogenic changes to the environment and human health outcomes, particularly in indigenous and other vulnerable communities. 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 and a Stanford EDGE Fellow.

Honors & Awards

  • 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)

Personal Interests

running, trivia, hiking, kayaking, crossword puzzle solving + constructing

All Publications

  • Harmonized nitrogen and phosphorus concentrations in the Mississippi/Atchafalaya River Basin from 1980 to 2018 SCIENTIFIC DATA Krasovich, E., Lau, P., Tseng, J., Longmate, J., Bell, K., Hsiang, S. 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 NATURE Hsiang, S., Allen, D., Annan-Phan, S., Bell, K., Bolliger, I., Chong, T., Druckenmiller, H., Huang, L., Hultgren, A., Krasovich, E., Lau, P., Lee, J., Rolf, E., Tseng, J., Wu, T. 2020; 584 (7820): 262-+


    Governments around the world are responding to the coronavirus disease 2019 (COVID-19) pandemic1, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with unprecedented policies designed to slow the growth rate of infections. Many policies, such as closing schools and restricting populations to their homes, impose large and visible costs on society; however, their benefits cannot be directly observed and are currently understood only through process-based simulations2-4. Here we compile data on 1,700 local, regional and national non-pharmaceutical interventions that were deployed in the ongoing pandemic across localities in China, South Korea, Italy, Iran, France and the United States. We then apply reduced-form econometric methods, commonly used to measure the effect of policies on economic growth5,6, to empirically evaluate the effect that these anti-contagion policies have had on the growth rate of infections. In the absence of policy actions, we estimate that early infections of COVID-19 exhibit exponential growth rates of approximately 38% per day. We find that anti-contagion policies have significantly and substantially slowed this growth. Some policies have different effects on different populations, but we obtain consistent evidence that the policy packages that were deployed to reduce the rate of transmission achieved large, beneficial and measurable health outcomes. We estimate that across these 6 countries, interventions prevented or delayed on the order of 61 million confirmed cases, corresponding to averting approximately 495 million total infections. These findings may help to inform decisions regarding whether or when these policies should be deployed, intensified or lifted, and they can support policy-making in the more than 180 other countries in which COVID-19 has been reported7.

    View details for DOI 10.1038/s41586-020-2404-8

    View details for Web of Science ID 000556239300002

    View details for PubMedID 32512578