9/25/24: Biostatistics Career Panel Fall 2024

Join us for a 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, September 25
Time: 11:00am - 12:00pm
Location: Berkeley Way West, 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

Aurélien Bibaut

I am a research scientist at Netflix, where I work on causal inference. Most recently, I designed methods for surrogate index design via meta-analyses of experiments, for nonparametric instrumental variables, and for large-scale choice models via low-rank methods. Prior to that, I was a PhD student in Biostatistics at UC Berkeley where I completed a dissertation on causal inference in adaptive experiments under the supervision of Mark van der Laan. My dissertation was awarded the Erich Lehmann award of the department of Statistics at UC Berkeley.

Caleb Miles

Dr. Caleb Miles is an assistant professor of biostatistics in the Columbia University Mailman School of Public Health. He received his PhD in biostatistics from Harvard, and did his postdoctoral fellowship in biostatistics at UC Berkeley. He works on developing semiparametric methods for causal inference and applying them to problems in public health. His applied work is largely in HIV/AIDS, psychiatry, anesthesiology, and drug abuse. His methodological research interests include causal inference, its intersection with machine learning, mediation analysis, transportability/generalizability, and measurement error.

Wenjing Zheng

Wenjing is a principal data scientist and data science manager at Roblox, leading the Ecosystems and Learning Platforms data science team. Before this, she was a staff data scientist at Netflix, leading the development and application of causal inference methods to inform user growth strategies. Before joining the industry, Wenjing was a researcher at the UC Berkeley Biostatistics Department. Wenjing obtained her Ph.D. in Biostatistics at UC Berkeley, with a research emphasis on targeted machine learning and causal inference.

Xiudi Li

Xiudi Li is an assistant professor in the Division of Biostatistics at UC Berkeley School of Public Health. He received his Ph.D. from the Department of Biostatistics at the University of Washington in 2022. Before joining Berkeley, he was a postdoctoral researcher in the Department of Biostatistics at Harvard T.H. Chan School of Public Health.

Yue You

I am Yue You, a data scientist at Waymo (a self-driving car company). I've been working on robust estimations, quantitative analysis, and optimization in the tech industry. I received my PhD in Biostatistics from UC Berkeley, advised by Prof. Mark van der Laan and Prof. Alan Hubbard, and my BS in Statistics from Fudan University in China.

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