My research interests include explainable machine learning, responsibility, evaluation and adversarial attacks.
Developing and maintaining open-source Python & R packages used for explainability of predictive models.
I received the John M. Chambers Statistical Software Award (2022).
- ECML PKDD
BScFooling Partial Dependence via Data PoisoningIn: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2022
Note: This paper is the core of our BSc thesis defended at Warsaw University of Technology.
SAManipulating SHAP via Adversarial Data Perturbations (Student Abstract)In: AAAI Conference on Artificial Intelligence (AAAI), 2022
MLOSSdalex: Responsible Machine Learning with Interactive Explainability and Fairness in PythonJournal of Machine Learning Research, 2021