Krzysztof Kępczyński

PhD candidate in Probability Theory and Experienced Applied Mathematics with a demonstrated history of leadership, risk management, and AI/ML-driven modeling. Skilled in applying advanced mathematical techniques and data-driven insights to support strategic decisions in finance, risk, and investment.

About me

As an applied mathematician, I am driven to solve complex business problems. I leverage my mathematical expertise to develop and implement effective risk management strategies and ensure the accuracy and reliability of statistical and machine learning (ML) models.

In the business world, I am a manager in the Credit Risk Models Validation Team at VeloBank. In this role, I oversee a team responsible for ensuring the accuracy and reliability of the bank's risk management models.

I am also deeply interested in scientific research in the field of probability theory. I am currently a PhD candidate in probability theory at the Mathematical Institute of the University of Wrocław, under the supervision of prof. Krzysztof Dębicki. My research is centered on the topic of "Asymptotics of functionals of Gaussian and Lévy processes with a view towards risk and queueing models," where I study the tail behavior and extreme value analysis of these stochastic processes.

Research Interests

Stochastic processes and queues. Gaussian processes - Lévy processes - Asymptotic techniques - Large deviations, Large-buffer asymptotics, Many-sources asymptotics.
Financial models. Insurance models - Risk analysis - Ruin probabilities.

Professional Interests

Leadership & Communication. Team Management and Mentoring - Interdepartmental Collaboration - Critical Thinking and Problem Solving - Adaptability and Agile Mindset.
Risk Management. PFSA and EBA Guidelines - IFRS 9 and IRRBB standards - Recommendations W, R, and G of the PFSA.
Mathematical Modeling. Artificial Intelligence, Machine Learning, eXplainable Artificial Intelligence, Time Series, Hidden Markov Models - Financial Engineering - Genetic Algorithms - Monte Carlo Simulation.

Teaching Activities

Courses on Mathematical Modeling. Mathematical Modeling in Python.
Courses on Financial Mathematics. Financial Engineering 2, Stochastic Financial Mathematics, Introduction to Financial Engineering.
Courses on Statistics. Time Series, Statistics, Introduction to the Practice of Statistics.
Other activities. Supervision of the Student Organization for Applied Mathematics (KNZM).