About Me
I am a theoretical physicist and Assistant Professor in the Faculty of Electronics at the Military University of Technology in Warsaw. I work at the intersection of theoretical physics and data science, using the geometric and information-theoretic structures of quantum mechanics to build and analyse methods for machine learning and data analysis.
Teaching is a large part of what I do. Over the years I have lectured across a range of institutions — SGH Warsaw School of Economics, Kozminski University, Cardinal Stefan Wyszyński University and the Military University of Technology — and across an equally wide range of subjects: from programming and real-time data analytics, through statistics, data mining and financial engineering, to quantum machine learning and quantum technology. I also mentor within the quantum-computing community as part of QPoland / Quantum AI Foundation.
Alongside academia, I have spent years as a practitioner. I worked as a Big Data / MLOps engineer in the Credit Risk department of PKO BP, building production Python tooling for data scientists (Kedro, Pandas, Polars, Spark) on GCP with Airflow, MLflow and Seldon, and earlier as a data analyst and data engineer designing real-time processing pipelines with Kafka, Spark and Flink. That engineering background keeps my research grounded in what actually runs on real systems.
Today I also work as a Quantum Machine Learning Engineer at finQbit, where I develop quantum models for derivative pricing. My research more broadly asks when quantum data encodings genuinely outperform classical methods, and when they do not — a question I explore both theoretically and on real hardware, in work such as The Inverse Born Rule Equivalence (2026) and Option Pricing on Noisy Intermediate-Scale Quantum Computers (2026), the latter with Prof. Jacob Cybulski (Deakin University). This sits within a longer-running program I am developing with Cybulski, Quantum Information Field Theory (QIFT).
My path has taken me from particle physics (a PhD on neutrino oscillations) through topological analysis of biomolecules (Scientific Reports, 2018) and graph-theoretic methods for anomaly detection, to quantum machine learning — with geometric and information-theoretic structure as the thread tying it together. Before all of it I trained for a decade as a musician, which is still how I think about structure, pattern and improvisation.