3/19/25: Biostatistics Career Panel Spring 2025

Join us for our Spring Biostatistics Career Panel sponsored by CTML! We’re thrilled to bring you a career panel featuring leading figures in biostatistics. Dive into their career stories, explore their research, and their unique perspectives on the field. This is your chance to engage in lively discussions and discover the many exciting paths within biostatistics!

Date: Wednesday, March 19th

Time: 12:00pm - 1:30pm

Location: Berkeley Way West, 5th Fl, Rm 5401

Meet our Chair

Alejandro Schuler

Alejandro Schuler is an Assistant Professor in Residence at UC Berkeley Biostatistics. His research focus is on developing methods for clinical decision-making in the real world that are economically or clinically necessary, statistically rigorous, and frictionless from the user perspective. He completed his Ph.D. at Stanford in 2018 and worked as a postdoc with CTML before starting on the faculty. Read More HERE

Meet our Panelists

Alejandra Benitez

I am a former doctoral student at UC Berkeley, advised by Dr. Maya Petersen. My research interests are in constrained optimization problems in medicine and public health, using machine learning and causal inference approaches. I started working at Genentech in July 2020 as a statistical scientist on a variety of projects in different therapeutic areas. In my work today I think critically about translating medical questions into statistical and causal questions, and I spend a lot of time learning about disease burden. During my time at UC Berkeley my research focused on viral load testing strategies that demonstrated potential cost reductions for patients and providers. I also collaborated with the UCSF Preterm Birth Initiative (PTBi) East Africa project, a cluster-randomized intervention to improve maternal-infant outcomes.

Andy Wilson

My name is Andy Wilson and I have a PhD in Public Health and an MStat in Biostatistics, both from the University of Utah School of Medicine. I've been a statistician for most of my career, though I sometimes introduce myself as a recovering statistician. What I mean is I've had a conversion, about the midpoint of my career, realizing the absolute necessity of causal methods to be able to learn from data. I'm currently the Head of Innovative RWD Analytics at Parexel, a very large CRO. I've been here for about 7 years now. I lead efforts to advance real-world evidence and causal inference in clinical research (mostly for pharma clients). My role is also part internal and part external roadshow -- evangelizing and teaching the importance of these innovative, yet rigorously proven, methods in causal analysis. I have a particular fondness for UC Berkeley—not just for its incredible biostatistics program, but also for its deep contributions to Targeted Learning, which has profoundly influenced my thinking and work. Click here to get insight on Andy Wilson's Journey!

Lauren Eyler Dang

Dr. Lauren Eyler Dang joined the NIAID Biostatistics Research Branch as a mathematical statistician in 2023. She obtained an MD from University of California, San Francisco, and an MPH and PhD in Biostatistics from University of California, Berkeley. Her research at NIAID focuses on applied and methodological causal inference problems and global infectious disease research.

Nima Hejazi

I’m an Assistant Professor of Biostatistics at the Harvard T.H. Chan School of Public Health, where I began my faculty career in 2022. Prior to this I was a postdoctoral researcher (2021-2022) and a doctoral student in biostatistics at UC Berkeley (2016-2021). My research program explores how advances in causal inference, statistical machine learning, and computational statistics can empower and catalyze discovery in the biomedical and public health sciences. Specific areas of recent emphasis have included causal mediation analysis; efficient inference in studies using tiered or two-phase, outcome- or auxiliary-dependent sampling designs; and the causal effects of continuous treatments under network interference. I’m also passionate about statistical instrumentation—high-performance computing, open-source software for statistics, and reproducible and transparent statistical data science. My work is usually motivated by applied science problems from observational studies and clinical trials of investigational therapeutics and preventives for infectious diseases, including COVID-19, TB/HIV co-infection, and PASC (or “long COVID”).