Bio

I am a postdoctoral researcher at the Onnela Lab in the Department of Biostatistics at Harvard T.H. Chan School of Public Health. I am working on methods for digital phenotyping and its applications with Beiwe platform.

My statistical methods interests include: methods for processing, features extraction and analysis of accelerometry data, power estimation in complex settings, functional regression methods, machine learning, R software development.

I received PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health in December 2021. I received my Bachelor’s and Master’s degrees in Mathematics from Wroclaw University of Science and Technology in Poland in 2013 and 2015. Prior to joining Johns Hopkins, I worked as a research assistant at Indiana University and as an analyst for Opera Software Internet browser company.

In my free time, I enjoy connecting with people and running. I completed my first full marathon in 2017 in Denver, CO.

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Recently

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Projects

Upstrap for estimating power and sample size in complex models

We evaluate the power and sample size estimation properties of the upstrap resampling method.

Adaptive empirical pattern transformation (ADEPT)

We propose adaptive empirical pattern transformation (ADEPT), a fast, scalable, and accurate method for pattern segmentation in time-series.

‘adeptdata’ R package: Raw Accelerometry Data Sets and Their Derivatives

Package adeptdata was created to host raw accelerometry data sets and their derivatives.

‘runstats’ R package: Fast Computation of Running Statistics for Time Series

Package runstats provides methods for fast computation of running sample statistics for time series via Fast Fourier Transform.

Brain connectivity-informed regularization methods for regression

We propose riPEER regularization method to estimate association between the brain structure features and a scalar outcome in a regression model while utilizing additional information about structural connectivity between the brain regions.

Teaching

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