Prof. Alan Hubbard
Biography: Dr. Alan Hubbard is Professor of Biostatistics, Head of the Division of Biostatistics at UC Berkeley, and Head of data analytics core at UC Berkeley SuperFund. His current research interests include causal inference, variable importance analysis, statistical machine learning, estimation of and inference for data-adaptive statistical target parameters, and targeted minimum loss-based estimation. Research in his group is generally motivated by applications to problems in computational biology, epidemiology, and precision medicine.
Biography: Jeremy Coyle is a postdoctoral scholar in Biostatistics at UC Berkeley. His research interests include cross-validation, resampling estimators, and optimal treatment parameters. Jeremy has applied machine learning methods to a variety of applications, including stove use monitoring, treatment assignment for victims of traumatic injury, and the translation of raw sensor data into meaningful estimates.
Wilson (Weixin) Cai
Biography: Wilson is a second-year graduate student in the Division of Biostatistics at UC Berkeley and is jointly advised by Profs. Alan Hubbard and Mark van der Laan. His research interests lie in statistical machine learning and causal inference. Prior to Berkeley, he obtained a Bachelor’s (B.Sc.) degree in Statistics from the Univeristy of Hong Kong.
Biography: Nima is a Ph.D. student in the Division of Biostatistics, where he is jointly advised by works Profs. Alan Hubbard and Mark van der Laan. His research interests encompass varied aspects of causal inference and nonparametric statistics, with a focus on the development of robust methods for addressing inference problems arising in precision medicine, computational biology, survival analysis, and clinical trials.
Chris J. Kennedy
Biography: Chris is a biostatistics Ph.D. student with interests in RCTs, machine learning, and targeted causal inference applied to precision medicine, cancer, and public health. He works with Alan on the varimpact R package and health prediction for trauma patients (e.g. traumatic brain injury). He also co-maintains the SuperLearner R package, employs high performance computing (Savio cluster, Amazon EC2, XSEDE), and is affiliated with D-Lab, Berkeley Institute for Data Science, and the Integrative Cancer Research Group.
Biography: Jonathan is a musician returning to his mathematical roots, pursuing a renaissance-view of marrying the endeavors of humanity in the face of practical specialization. His interests within biostatistics revolve around being an advocate for scientists who have critical questions and for those who need a voice from someone trained in the field of statistics, who will look at the science and literature as well as issues not related to science, to shed light on health issues, rather than simply taking refuge in the most highly promoted establishment views.
Biography: Ivana is a second-year graduate student in the Division of Biostatistics at UC Berkeley. Broadly, her research interests span causal inference, high-dimensional data, machine learning, and semiparametric theory.
Google Scholar: profile
Biography: Sara is a Biostatistics Ph.D. candidate in her final year of study at UC Berkeley. She received her B.A. in Computer Science and Psychology from Duke University and subsequently worked as a researcher in brain imaging labs at Duke and Emory Universities. Her current research focuses on machine learning methodology for the prediction of adverse health-related outcomes in trauma care patients, prediction of mass trauma events using social media data, data visualization, and statistical software package development. She also currently works as a consultant at Genentech in South San Francisco.
Rachael V. Phillips
Rachael is a Biostatistics Ph.D. student. She received an MA in Biostatistics in May 2018 from UC Berkeley. She graduated with Cum Laude honors from Texas Tech University in 2015, receiving a BS in Biology with a Chemistry minor and a BA in Mathematics with a Spanish minor. Rachael’s current research involves the application of machine learning methods, nonparametric statistical estimation, statistical computing, and causal inference to large biological datasets to solve real-world problems in human health, molecular biology, and meta-analysis. She is also passionate about online mediated education and responsible conduct in research. Rachael actively works with the UC Berkeley Superfund Research Center and the National Cancer Institute Division of Cancer Epidemiology and Genetics.
Andre Kurepa Waschka
Biography: Andre is a fourth-year Ph.D. student in Statistics at UC Berkeley. He finished his M.A. in Biostatistics under Dr. Hubbard in 2016. He graduated from North Carolina State University with a B.S. in Applied Mathematics, B.S. in Economics, and a minor in Statistics.
Prof. Romain Pirracchio
Biography: Prof. Pirracchio is a French M.D., Ph.D., hailing from Paris. He obtained his M.D. in 2003, with a specialization in Anesthesiology and Critical Care Medicine. In 2008, he obtained a Master’s degree in Medical Research Methodology and Biostatistics. He completed his doctoral studies in the Department of Biostatistics and Medical Informatics (DBIM, unité INSERM U-1153) at Hôpital Saint Louis, Paris, France in 2012 under the guidance of Prof. Sylvie Chevret. In 2012-2013, He spent a year as a postdoctoral fellow in Biostatistics in the School of Public Health at the University of California, Berkeley, where he worked under the supervision of Prof. Mark J. van der Laan and Prof. Maya L. Petersen. Back in Paris, he was the director of the surgical and trauma ICU at European Hospital Geroges Pompidou (2013-2015) and a researcher in Biostatistics at the INSERM U-1153 unit. In January 2015, Dr. Pirracchio joined the Department of Anesthesia and Perioperative care at the San Francisco General Hospital & Trauma Center (UCSF) as Associate Professor. Since September 2016, he has been at the European Hospital Geroges Pompidou in Paris, serving as Full Professor and Vice Chair for ICUs. He is also Adjunct Associate Professor at UCSF and affiliate to the Division of Biostatistics at UC Berkeley.