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.
Alissa Gordon is currently a 2nd year MA/PhD student in Biostatistics at the University of California, Berkeley, mentored by Dr. Alejandro Schuler. Her research interests broadly lie in causal inference and machine learning. She aims to address areas in clinical research where current methods are insufficient through the development of creative, novel methods. Recent work has been in the development of non-parametric sensitivity analysis for hybrid control trials.
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.
Kaiwen Hou is a statistician. He mostly reflects on how probabilities move and change shape, using geometry to guide the flow toward carefully chosen targets that bring him closer to the truth.
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 Landsiedel is currently a PhD candidate in Biostatistics at the University of California, Berkeley, mentored by Mark van der Laan and Laura Balzer. She previously earned her MA in Biostatistics at UC Berkeley, as well as a BS in Statistics and a BA in Economics from UCLA. Her research interests primarily lie in targeted machine learning, causal inference, survival analysis, semi-parametric efficiency theory, and methods for handling missing and censored data. She has previously contributed to applied research examining the relationship between alcohol consumption and incident TB...
Kaitlyn Lee is a PhD candidate in Biostatistics at the University of California, Berkeley, mentored by Dr. Alejandro Schuler. She previously earned her MA in Biostatistics at UC Berkeley and her BA in Physics from Harvard University. Her primary research interest lies in developing causal inference methods that leverage machine learning and semiparametric statistics to create statistically rigorous solutions for critical problems in health and social policy. Her current work has focused on developing new machine learning algorithms that offer flexible and computationally efficient...
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.
Joy Zora Nakato is a PhD student in Biostatistics at the University of California, Berkeley mentored by Laura Balzer and Mark van Der Laan. Her research interests include using targeted machine learning and causal inference to address challenges that arise during design and analysis of clinical trials and observational studies. She is currently working on efficient estimation of causal effects in Cluster Randomized trials addressing differential measurement and mediation with an application to uptake of HIV biomedical prevention among high risk populations in rural Uganda and Kenya...
Sky Qiu is a PhD student in Biostatistics at the University of California, Berkeley, mentored by Alan Hubbard and Mark van der Laan. His research interests primarily lie in causal inference and targeted learning. Recent work has been in extending the statistical method for hybrid randomized-observational data to survival outcomes, as well as developing scalable versions of highly adaptive lasso.
