Przemyslaw Marcowski

PhD Computational Scientist

I specialize in data science and analytics. I use data to find answers, solve problems, and make things more interesting. Data is a reflection of us—our biology, behaviors, choices, and interactions. I like discovering the stories it can tell.

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Skills

R
Python
SQL
Git
Shiny
Data Science
Data Analysis
Statistical Modeling
Machine Learning
Data Mining
Data Visualization
Scientific Programming
Experimental Design
Quantitative Research
Behavioral Science
Decision Science
Problem Solving

Showcase Projects

Cross-cultural validation in the International Sex Survey (ASRS)

Cross-cultural ADHD Assessment in International Sex Survey

Conducted cross-cultural validation of the Adult ADHD Self-Report Scale (ASRS) Screener across 42 countries as part of the International Sex Survey (2021/2022) team. Performed data preparation, psychometric evaluation, and cross-cultural comparisons. Analysis included Confirmatory Factor Analysis (CFA), measurement invariance testing, reliability analysis, and examination of group differences.

R
Statistical Analysis
Psychometrics

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Effort-Based Choice Modeling

Analyzed and modeled effort-based choices in economic decision-making. Formulated a novel choice model and compared it to existing models to explain how effort influenced perceived value. Executed agent-based simulations for model recovery and parameter estimation. Performed data preprocessing, statistical analysis, model selection, and result visualization.

R
Behavioral Economics
Statistical Modeling

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Model Recovery Analysis for Intertemporal Choice

Performed a model recovery analysis to validate the reliability of intertemporal choice models. Compared models, simulated choice behavior using agents, and performed recovery analysis to verify accurate recovery of true model parameters from simulated data.

R
Simulation
Model Validation

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Two-Armed Bandit Game with Reinforcement Learning Analysis

Developed a Two-Armed Bandit game application where players chose between two arms offering different reward potentials. Analyzed the players' choices using a reinforcement learning model, and visualized the decision history characteristics.

R
Shiny
Reinforcement Learning

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Dog Growth Prediction Model and Application

Built a Bayesian predictive model for dog growth trajectories based on the von Bertalanffy growth curve. Implemented the model in a Shiny app that generates personalized growth predictions based on user input.

R
Shiny
Bayesian Modeling

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Maze Generation and Solving

Generated random mazes using the Recursive Backtracking algorithm and solved them using a chosen path-finding algorithm. Visualized the generated mazes and their solutions, allowing for comparisons of the strengths and weaknesses of the different path-finding algorithms.