Andy Kim is currently a second year MA/PhD student in Biostatistics at the University of California, Berkeley, mentored by Dr. Alan Hubbard. His research interests primarily lie at the intersection of personalized medicine and causal inference. He is currently working on an unsupervised clustering machine learning project with D-SINE.
Sajia Darwish is a master’s student in Epidemiology and Biostatistics at the UC Berkeley School of Public Health. Her research interests center on using data analytics, statistical methods, and machine learning to understand and solve problems in health. As a graduate student researcher at CTML, Sajia works under the supervision of Alan Hubbard to harmonize data and apply statistical methods and machine learning techniques to analyze the relationships between maternal milk composition and child growth. She is also a special project researcher at UC Berkeley’s D-Lab and the American...
David Chen is a PhD student in Biostatistics at UC Berkeley, working with Maya Petersen and Mark van der Laan. He currently leads the JICI working group on Competing Risks, which pushes forward the targeted learning and causal roadmap framing of competing risks analysis via software development and real world data applications. His research interests lie in the intersection of causal inference, machine learning, and semi-parametric estimation; in particular the effective translation and practice of these methods.
Kirsten Landsiedel is a second-year MA/Ph.D. student in Biostatistics at the University of California, Berkeley. Advised by Professor Alan Hubbard, she currently works with Professors Mark van der Laan and Maya Petersen on efficient estimation in two-stage sampling designs and adaptive sampling designs.
Lauren Liao is currently a doctoral student in Biostatistics at the University of California, Berkeley, mentored by Yeyi Zhu, Sam Pimentel, and Alan Hubbard. Her research interests broadly encompass observational study design and analysis in support of understudied, underrepresented, and underreported populations. She currently leads the statistical analysis in Mexican COVID-19 to identify subpopulations with highest risk of severe outcomes. Recent work has been in treatment prediction for gestational diabetes using supervised machine learning.
Nerissa Nance is an Epidemiology PhD student at UC Berkeley, advised by Maya Petersen. She helps lead the JICI working group on applying the longitudinal causal roadmap to Danish registry data. Nerissa has also worked as a Senior Analyst at Kaiser Permanente Northern California's Division of Research, where she helped apply causal inference methods to study the impacts of antihypertensive treatment during pregnancy. She holds a Master's degree in Epidemiology and Biostatistics, also from Berkeley.
Nolan Gunter is currently a second year MA/PhD student in Biostatistics at the University of California, Berkeley, mentored by Dr. Alan Hubbard. Their research interests primarily lie in epidemiology, HIV, and causal inference. They are currently working on an economic clustering machine learning project with D-SINE.
Sky Qiu is a second-year MA/Ph.D. student in Biostatistics at the University of California, Berkeley, mentored by Professor Alan Hubbard. His research interests primarily lie in causal inference and targeted learning. He is currently working on the tlverse project.
Rachael V. Phillips is a PhD student in Biostatistics at the University of California at Berkeley, mentored by Prof. Mark van der Laan. Her current work includes clinical applications of the personalized online super learner, automated targeted learning (TL) development, and a TL demonstration project for the US Food and Drug Administration with Dr. Susan Gruber. She is an author of tlverse software, including super learner (sl3), and highly adaptive lasso (hal9001), and cross-validation (origami) packages. Rachael was integral in the development of a recently-approved course series...