About Me
I am a theoretical physicist and Assistant Professor at the Decision Analysis and Support Unit, SGH Warsaw School of Economics, and a researcher at the Faculty of Electronics, Military University of Technology (WAT). My work sits at the boundary between theoretical physics and modern data science — I develop mathematical frameworks that translate the formalism of quantum mechanics into practical tools for anomaly detection, time series analysis, and financial forecasting.
The central thread of my research is Quantum Information Field Theory (QIFT) — a framework I am building together with Prof. Jacob Cybulski (Deakin University, Australia). QIFT treats data not as a static vector but as a field of relational information, enabling encoding schemes that genuinely exploit quantum non-commutativity rather than merely emulating classical statistics in Hilbert space. The framework provides a principled foundation for quantum anomaly detection on tabular and graph-structured data, and directly challenges established assumptions in amplitude encoding — most recently formalised in our preprint The Inverse Born Rule Fallacy (arXiv, 2026).
My trajectory: PhD in particle physics (neutrino oscillations, family symmetries) → topological analysis of biomolecules (genus invariant, Nature Scientific Reports 2018) → graph-based community features for classification → quantum machine learning on NISQ hardware. This path reflects a consistent interest in geometric and information-theoretic structure as the source of predictive power in complex systems.
Beyond academia, I work as a Quantum Machine Learning Engineer at finQbit, deploying QML models on real quantum hardware for financial applications using Python and Julia. I am a member of the American Physical Society and QPoland.