Sylvia is a PhD student in Epidemiology at UC Berkeley, advised by Barbara Laraia and Alejandro Schuler. Her research interest lies in the intersection of precision health, causal inference and machine learning with robust uncertainty quantifications. Her work focuses on addressing and solving real-world challenges, such as evaluating causal effects of determinants of cellular aging as well as metabolic diseases. Sylvia also serves as a Data Scientist in the Division of Research at Kaiser Permanente.
Nolan Gunter is a Biostatistics PhD student at UC Berkeley, advised by Dr. Alan Hubbard, having completed the Biostatistics MA program in Spring 2023. Nolan has worked on unsupervised clustering for socioeconomic status with D-SINE. They also work as a long-term intern under Non-Clinical Biostatistics at Genentech/Roche.
Shalika is an Epidemiology Ph.D. student advised by Professor Maya Petersen. She previously earned a B.S. in Environmental Sciences and an M.A. in Biostatistics from UC Berkeley. Prior to graduate school, Shalika worked as a research associate at the Public Health Institute’s Alcohol Research Group. As a graduate student, she worked on The Fit Study under the supervision of Professor Kristine Madsen. Her current research focuses on understanding the mechanisms by which the Sustainable East Africa Research in Community Health (SEARCH) trial impacted mother-to-child transmission of HIV...
Yunwen Ji is currently a second year student in Biostatistics at the University of California, Berkeley, mentored by Alan Hubbard. Her research interests primarily lie in machine learning methodologies in precision medicine.
Andy Kim, MA is currently a PhD candidate 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.
Kirsten is a PhD student in Biostatistics at UC Berkeley, mentored by Mark van der Laan and Laura Balzer. Her research interests include targeted machine learning and causal inference with applications to complex, real-world data. She is currently working on improving the efficiency of estimators in adaptive resampling designs, estimating TB incidence in children and adolescents in Uganda, and better understanding the link between alcohol use and TB infection.
Kaitlyn Lee is a MA/PhD student in Biostatistics at the University of California, Berkeley mentored by Alejandro Schuler. Her research interests are broadly in causal inference, machine learning, and methods research. She is interested in developing methods to answer real-world questions about health and social policy in a statistically rigorous manner. Her current work focuses on causal inference with continuous treatment variables and modified treatment policies.
Yi Li is currently a PhD student in Biostatistics at the University of California, Berkeley, mentored by Mark van der Laan. His research interests primarily lie in causal inference, semi parametric estimation and network analysis. Recent work has been in adaptive design.
Lauren Liao is currently a doctoral candidate in Biostatistics at the University of California, Berkeley, co-advised by Sam Pimentel and Alejandro Schuler. Her research interests broadly encompass efficiently using prior studies to support causal study design and analysis in support of the underrepresented population. She currently leads the statistical methods development in prognostic-covariate adjustment with efficient estimators in randomized trial analysis. Recent work has been in treatment prediction for gestational diabetes using supervised machine learning.