I am a second year Ph.D. student in Doris Bachtrog's evolutionary and functional genomics lab. I am generally interested in understanding the dynamics of epigenetic modifications and their resulting biological consequences. My current research aims to link variation in the loss of heterochromatin, genomic regions that are gene-poor, repeat-rich, and typically enriched for silencing histone modifications, to variation in aging. Through the BBD training grant, I hope to integrate experimental and computational work to better understand the basis of longevity.
Amanda is a Ph.D. student in Computational Biology Graduate Group under Professor Lisa Barcellos. She earned her Master of Public Health from the UC Berkeley School of Public Health in 2016, and two S.B.s from MIT in Biological Engineering and Music in 2011. Amanda's research interests involve using whole-genome genotyping and methylation profiling data to better understand the mechanisms underlying autoimmune disorders and their related outcomes.
Karl is a PhD student in the UC Berkeley Statistics Department, advised by Prof. Bin Yu. He graduated from Pomona College in 2013 with a BA in mathematics and a focus in statistics. Karl’s research interests include applied statistics, bioinformatics, sparse modeling, and network analysis. More specifically, he has been working with the Celniker lab at LBNL to develop and understanding of how spatial gene expression guides embryo development in Drosophila.
Mary is a Ph.D. Student in the Division of Biostatistics in Berkeley's School of Public Health under Mark van der Laan. She earned her Masters in Biostatistics from UC Berkeley's SPH in 2016, and a B.A. in Applied Mathematics in 2008. Mary’s research interests include developing targeted estimators for parameters of interest of semi- and non-parametric models, including model spaces with non-testable assumptions allowing for causal inference. She is also interested in applying existing estimation techniques to observational and longitudinal studies with informative censoring and...
I'm interested in developing statistical and computational tools for addressing questions in population and evolutionary genetics. My recent work has focused on methods that use Next-Generation Sequencing data for mapping genes, identifying copy number variation in genomes, and inferring population demography. I have been using these methods to determine the genes that underly color and pattern in poison frogs, to find paralogy in the human genome with the 1000 Genomes data, and to understand climate change response in alpine chipmunks. I maintain all programs at...
Chris is interested in RCTs, targeted machine learning, and causal inference applied to precision medicine, particularly trauma. He works with Alan Hubbard on the varImpact package, health prediction for trauma patients (e.g. traumativ brain injury), and analysis of biometric waveform data. He also co-maintains the SuperLEarner R package, employed high performance computing (Savio sluster, Amazon EC2), and is a D-Lab consultant & instructor.
