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.

Download my CV.

Recently

• Our preprint “Wearable device and smartphone data can track ALS disease progression and may serve as novel clinical trial outcome measures” is out. This work investigates whether mobile applications and wearable devices can be used to quantify ALS disease progression through active (surveys) and passive (sensors) data collection.
  
  

• Our preprint “Performance analyses of step-counting algorithms using wrist accelerometry” is available online. This work evaluates performance of several modern wrist-accelerometry-based algorithms for step count estimation using a common dataset with various continuous walking trials.
  
  

• Our work “Estimating knee movement patterns of recreational runners across training sessions using multilevel functional regression models” is accepted. This work can serve as a reference for practitioners modeling repeated functional measures at different resolution levels in the context of biomechanics and sports science applications.
  
  

• Our paper “Comparison of Accelerometry-Based Measures of Physical Activity: Retrospective Observational Data Analysis Study” is published. This work provides comparison and harmonization mapping between minute-level accelerometry-derived measures (ActiGraph AC, MIMS, ENMO, MAD, AI).
  
  

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