Research Fellow, Digital Health Social Justice Project Lead
School of Social Welfare
Digital Health Social Justice
Caroline Figueroa, MD Ph.D., is a Postdoctoral Scholar at UC Berkeley School of Social Welfare. She obtained her MD degree and Ph.D. degree at the University of Amsterdam in the Netherlands. Her Ph.D. research took place at the University of Amsterdam and at the University of Oxford, where she studied cognitive and neurobiological vulnerability factors for recurrence of depression in patients remitted from Major Depressive Disorder. Current research interest is on digital interventions for depression, with an emphasis on developing cutting-edge innovations that tailor to the needs of...
Bioscience, Natural Resources & Public Health Library
Michael has worked at the UC Berkeley Library since 2001, and is currently the Public Health Librarian and Liaison to the School of Optometry at the Bioscience, Natural Resources & Public Health Library. Michael coordinates public health instruction at the library, and is responsible for the public health collection. Michael has a MLIS from San Jose State University, an MS in Geography from Oregon State University, and a BA in Geography from UC Berkeley. When not at work he lives out his fantasy of being a rock and roll drummer.
by Carly Karrick. Viruses play important roles in evolution and influence ecosystems and host health. However, isolating and studying them can be difficult. In lieu of using resource-intensive methods to concentrate viruses into a “virome,” bulk sequencing methods include data from all biological entities present in a sample. In this tutorial, we explore an approach to mine viral sequences from publicly available bulk RNA-Seq data. The output from this analysis paves the way for future statistical analyses comparing viral communities in different contexts. This approach can be applied to other datasets, including studies of human health.
Participants in this workshop will learn about some of the issues surrounding the collection of health statistics, and will also learn about authoritative sources of health statistics and data. We will look at tools that let you create custom tables of vital statistics (birth, death, etc.), disease statistics, health behavior statistics, and more.
Participants in this workshop will learn about some of the issues surrounding the collection of health statistics, and will also learn about authoritative sources of health statistics and data. We will look at tools that let you create custom tables of vital statistics (birth, death, etc.), disease statistics, health behavior statistics, and more.
Participants in this workshop will learn about some of the issues surrounding the collection of health statistics, and will also learn about authoritative sources of health statistics and data. We will look at tools that let you create custom tables of vital statistics (birth, death, etc.), disease statistics, health behavior statistics, and more.
Participants in this workshop will learn about some of the issues surrounding the collection of health statistics, and will also learn about authoritative sources of health statistics and data. We will look at tools that let you create custom tables of vital statistics (birth, death, etc.), disease statistics, health behavior statistics, and more.
by Paige Park. Our capacity to accurately predict social outcomes is increasing. Deep neural networks and artificial intelligence are crucial technologies pushing this progress along. As these tools reshape how social prediction is done, social scientists should feel comfortable engaging with them and meaningfully contributing to the conversation. But many social scientists are still unfamiliar with and sometimes even skeptical of deep learning. This tutorial is designed to help close that knowledge gap. We’ll walk step-by-step through training a simple neural network for a social prediction task: forecasting population-level mortality rates.
Marina is a master's student in the Health and Social Behavior division of the School of Public Health. She has extensive experience in ATLAS.ti and can help you get the most out of the program. She is passionate about data visualization, and is happy to help with related questions and questions on qualitative methods.
Monica is a third-year Ph.D. candidate in the Environmental Science, Policy, and Management program. She uses computational tools to study the evolution and ecology of agricultural plant pathogens. Previously, she worked on a data science team at a biotech company in Boston.
