Nick Seewald

Postdoctoral Fellow
Department of Health Policy and Management

Johns Hopkins Bloomberg School of Public Health

Hi, I’m Nick.

I develop statistical methodology for the design and analysis of randomized trials which yield complex longitudinal data for precision health. Specifically, I focus on methods that aid in the construction of decision rules which specify for whom to provide what treatment and when. I take a broad approach to this, seeking to make an impact in both statistics and domain sciences from a project’s inception to the dissemination of results.

Currently, I am a postdoctoral fellow in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health, working with Elizabeth Stuart, Ph.D. and Beth McGinty, Ph.D. on causal inference for health policy evaluation.

My Ph.D. was supervised by Daniel Almirall, Ph.D..

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Interests

Dynamic Treatment Regimens
Sequential, Multiple-Assignment Randomized Trials
Randomized Trial Design
Complex Longitudinal Data

Education

PhD in Statistics, 2021

University of Michigan
MA in Statistics, 2018

University of Michigan
MS in Biostatistics, 2015

University of Michigan
BS in Mathematics, 2013

University of Notre Dame

Dissertation Defense

I’m finally defending! Join the fun on Zoom on May 10 at 10am ET. Contact me for details.

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I’ve joined JHSPH!

I’m happy to announce that I’ve begun a postdoctoral fellowship with Elizabeth Stuart and Beth McGinty at the Johns Hopkins Bloomberg School of Public Health. I’ll be working on developing methods for causal inference in health policy evaluation.

Outstanding Graduate Student Instructor Award

I am thrilled and honored to have won an Outstanding Graduate Student Instructor Award from Rackham Graduate School at the University of Michigan, which is given annually to twenty GSIs across the University.

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