Przemyslaw Biecek
Director at Mi2DataLab
Warsaw, Masovian Voivodeship, Poland
Summary
🤩
Rockstar🎓
Top SchoolPrzemyslaw Biecek is a seasoned AI and statistics leader with over a decade of experience bridging academia and industry, currently directing the Centre for Credible AI and leading MI2DataLab at Warsaw University of Technology. He combines deep expertise in biostatistics, explainable AI and human-centered data visualization with hands-on open-source contributions to prominent tools like DALEX and survminer that advance model interpretability. A tenured professor and founder of multiple initiatives, he builds practical workflows for responsible ML—emphasizing risks like bias and model drift alongside deployment. His background spans roles from principal data scientist at Samsung to a Fulbright stint at Harvard Medical School, and he holds a PhD and habilitation in biostatistics plus an MBA, reflecting both technical rigor and strategic perspective. Notably, he treats AI as “atomic energy”: powerful when harnessed responsibly, dangerous if ignored.
12 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy (PhD) Biostatistics, Doctor of Philosophy (PhD) Biostatistics at Wrocław University of Science and TechnologyMaster of Business Administration (MBA), Master of Business Administration (MBA) at Warsaw University of Technology Business SchoolHabilitation Biostatistics, Habilitation Biostatistics at Nałęcz Institute of Biocybernetics and Biomedical Engineering PAS
English
Github Skills (8)
data-visualizations10
survival-analysis10
data-visualization10
data-visualisation10
explainable-artificial-intelligence10
ggplot10
statistical-models10
r10
Programming languages (11)
Github contributions (5)
ModelOriented/DALEX
Feb 2018 - Jan 2023
Role in this project:
Data Scientist
Data ScientistContributions:2 releases, 28 reviews, 351 commits in 4 years 11 months
Contributions summary:Przemyslaw appears to be implementing and documenting features within the "dalex" library, a tool for explainable AI. The commits involve the creation and improvement of functions for plotting model predictions, building variable profiles, and calculating variable importance metrics. They are directly contributing to core aspects of the library related to model interpretability and providing tools for analyzing machine learning models.
xaishapagnosticblack-boxexplainable-ml
kassambara/survminer
Jan 2017 - Sep 2019
Survival Analysis and Visualization
Role in this project:Data Scientist
Data ScientistContributions:98 commits, 31 PRs, 72 comments in 2 years 7 months
Contributions summary:Przemyslaw primarily contributed to the development of a forest plot visualization function (`ggforest`) for Cox Proportional Hazards models. This function was enhanced over multiple commits, adding features such as axis labels, plot titles, and customizable font parameters. Further contributions include improvements to diagnostic plots for Cox models, implementing and expanding existing plotting functionalities.
survival-analysissurvivaldata-visualizationvisualization
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