Cohort 4: 2019-2020

Lauren Eyler
Lauren is a Biostatistics PhD student working with Dr. Alan Hubbard. She received a BS from Yale University in 2011, an MPH from UC Berkeley in 2015, an MD from UCSF in 2016, and is currently in the research years of her general surgery residency at UCSF. Her research focuses on developing biostatistical tools to advance equitable, data-driven surgical systems development in low- and middle-income countries.

Heather Amato
Heather is a PhD student in Environmental Health Sciences working with Dr. Jay Graham. She received her MPH in Global Environmental Health from Emory University in 2016 and a BA in Psychology from Kenyon College in 2013. She spent the last two years as a data analyst at Tracking California, a CDC-funded program that mobilizes environmental health data to inform public health action. Her current research aims to quantify impacts of water and sanitation infrastructure on the carriage of infectious pathogens that are resistant to antibiotics. She hopes to quantify the abundance and diversity of antimicrobial resistance genes through space and time with machine learning and spatial statistics. Heather is also interested in using novel machine learning techniques to reduce bias in existing metagenomics methods used for identifying resistance genes.

James Duncan
James is a first-year Ph.D. student in Biostatistics and Domain Consultant with Berkeley Research Computing. Before starting the Ph.D., he received his M.A. in Statistics at UC Berkeley where he developed an interest in high dimensional data analysis and causal inference, and later worked as a contributing developer to the nCompiler R package under the guidance of Professor Perry de Valpine. More recently, he has been working with researchers at UCSF to analyze the medical costs of homelessness and developing a PySpark package to analyze very wide datasets with applications including genomics and neuroscience. James hopes to develop tools to make complex, computationally intensive data analyses more intuitive and routine for researchers with causal questions in mind.

David McCoy
David McCoy is a Ph.D. student in the Environmental Health Sciences (EHS) Department working with Professors Alan Hubbard and Martyn Smith. He received his Master of Science in Epidemiology from the London School of Hygiene and Tropical Medicine in 2017 and has been working with the University of California, San Francisco (UCSF) as a Data Scientist. He is interested in understanding how exposure to pollution, such as persistent organic pollutants, heavy metals, and air pollution leads to compounding oxidative stress on an organism and subsequent disease. Because of the complex causal networks in such data, he is interested in exploring new statistical methods to derive causal inference from machine learning approaches and applying semiparametric modeling to toxicology data. Broadly, he investigates the statistical methods needed to operationalize the theories of Exposomics. In doing so, he works closely with the UC Berkeley Superfund Research Program, the National Cancer Institute Division of Cancer Epidemiology and Genetics, and the United Kingdom Biobank.

Robert Schell
Robert is a Health Policy PhD student in the School of Public Health advised by Professors Lia Fernald and William Dow. He received an M.S. in Applied Economics with a concentration in Applied
Econometrics and Quantitative Analysis from Cornell University in 2019. In 2016, he graduated summa cum laude from Denison University with a B.A. in Economics and a minor in German. Robert’s research focuses on emerging issues in public health, such as the obesity epidemic and the development of resistance to antimicrobials critically important to human health. His current research includes applying both machine learning techniques and causal inference methods from econometrics to predict the efficacy of changes to the SNAP program for different families across the country. He is also working on an ongoing project in collaboration with Cornell University’s Weill Medical School determining the effectiveness of cash transfers and daily self-weighing on reducing obesity rates among lower income adolescents in Harlem.

Jennifer Head
Jennifer is a PhD student in Epidemiology advised by Professor Justin Remais. She has previously received a B.S. in Chemical Engineering from Washington University in St. Louis and an MPH in Global Environmental Health from Emory University. Her research seeks to understand disease transmission at the human-animal-environment interface, by integrating causal inference and targeted learning methodology with 'big' surveillance data. She is currently working with California Department of Public Health on characterizing the environmental drivers of coccidioidomycosis (Valley Fever) in California.