Andy Kim is currently a first 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.
My research interest span non/semi-parametric theory, causal inference and machine learning. Most of my current work involves complex dependent settings (dependence through time and network), reinforcement learning, and adaptive sequential designs. I am also interested in model selection criteria, optimal individualized treatment, online learning and software development.
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.
Maxwell Murphy is a PhD student in Biostatistics working with Prof. Mark van der Laan. His research currently consists of developing targeted learning methods for the analysis of clustered data. His research interests primarily lie in developing new tools and integrating state of the art methods in targeted learning. His other research interests also include developing methods for integrating genomics and epidemiological data to better understand malaria transmission dynamics.
Nerissa Nance is an Epidemiology PhD student at UC Berkeley, advised by Maya Petersen and Jen Ahern. She helps lead the JICI working group on applying longitudinal causal inference methodology to questions surrounding the effects of second-line diabetes medication use. Nerissa has also worked as a Senior Analyst at Kaiser Permanente Northern California's Division of Research, where she helped apply causal inference techniques to measure 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 first 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.