Pawel Morzywolek

Pawel Morzywolek is a Postdoctoral Researcher in the Department of Statistics at the University of Washington. He holds dual appointments as a UW Data Science Postdoctoral Fellow and a PIMS-Simons Postdoctoral Fellow. His research focuses on causal inference and statistical inference for infinite-dimensional parameters, with applications to infectious disease prevention strategies and medical decision-making in critical care settings.

His academic background includes:

• PhD in Statistical Data Analysis (2023) from Ghent University
• MSc in Mathematics (2015) from ETH Zurich
• BSc in Mathematics (2014) from ETH Zurich

Morzywolek's research bridges theoretical statistics with practical healthcare applications. He specializes in developing methods for estimating heterogeneous treatment effects and optimizing dynamic treatment regimes, with particular focus on determining optimal timing for medical interventions in critical care. His work combines semiparametric theory with statistical machine learning to address complex problems where traditional methods fall short, especially in settings with observational data and competing risks.

His publication record shows a strong methodological focus with immediate medical applications. The research demonstrates progression from theoretical foundations of causal inference to specific implementations for healthcare challenges, particularly in intensive care medicine where treatment timing decisions can significantly impact patient outcomes.

Morzywolek currently holds two prestigious fellowships that support his research agenda:

• UW Data Science Postdoctoral Fellow
• PIMS-Simons Postdoctoral Fellow

As a postdoctoral researcher working with Professor Alex Luedtke, Morzywolek develops both theoretical statistical methods and practical software implementations. He maintains active research collaborations with medical researchers to apply advanced causal inference techniques to real-world healthcare challenges, particularly in nephrology and infectious disease prevention. His GitHub repositories provide reproducible code for his published research, demonstrating commitment to open science practices.

Morzywolek's work on dynamic treatment regimes has particular relevance for acute clinical scenarios where optimal timing of interventions is crucial. His research contributes to the growing field of precision medicine by developing statistical frameworks that can translate complex observational data into actionable clinical decision rules.