About Me

I’m a Computer Science student at the Jagiellonian University, currently pursuing my master’s degree. My main interests focus on machine learning and computer vision, especially their applications in medical imaging and data analysis. At the same time, I’m passionate about software engineering — I enjoy low-level programming in C++, designing systems and architectures, and creating practical tools that actually solve problems. I like understanding how things work and turning ideas into something real and useful.

Education

  • Master’s in Computer Science
    Jagiellonian University • Krakow, Poland
    Focused on machine learning and computer vision, with involvement in practical research projects and publications. Continued developing programming skills, including low-level languages such as assembler.
  • Bachelor’s in Computer Science
    Jagiellonian University • Krakow, Poland
    Developed a strong foundation in computer science and mathematics, along with practical experience in software development for web, mobile, and desktop platforms.

Experiences

  • Software Engineer Internship
    HERE Technologies
    Refactored legacy code in Java and Swift, improving structure and readability, and updated unit and integration tests to increase coverage and reliability. Optimized older C++ components by enhancing memory usage and execution time. Collaborated closely with the team in Agile ceremonies and daily work to clarify requirements and align technical decisions. Used GitLab for version control and CI, and worked with internal tooling for builds and testing.

Skills

Machine Learning & AI

Machine Learning, LLMs, NLP, PyTorch, NumPy, pandas

Programming Languages

C++, Python, .NET, C#, Java, Shell, Assembler

Software Engineering

OOP, SOLID, Design Patterns, Unit Testing, PyTest, Google Test

DevOps & Tools

Git, GitLab, CI/CD, Docker, Jira, Agile

Systems

Linux, macOS, Windows

Databases

SQL

Mathematics

Statistics, Calculus, Linear Algebra

Last publications

  • MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging

    Authors: Kacper Marzol, Ignacy Kolton, Weronika Smolak-Dyżewska, Joanna Kaleta, Marcin Mazur, Przemysław Spurek

    arXiv preprint arXiv:2509.16806 • September 2025

    Multi-modal three-dimensional (3D) medical imaging data, derived from ultrasound, magnetic resonance imaging (MRI), and potentially computed tomography (CT), provide a widely adopted approach for non-invasive anatomical visualization. Accurate modeling, registration, and visualization in this setting depend on surface reconstruction and frame-to-frame interpolation. Traditional methods often face limitations due to image noise and incomplete information between frames. To address these challenges, we present MedGS, a semi-supervised neural implicit surface reconstruction framework that employs a Gaussian Splatting (GS)-based interpolation mechanism. In this framework, medical imaging data are represented as consecutive two-dimensional (2D) frames embedded in 3D space and modeled using Gaussian-based distributions. This representation enables robust frame interpolation and high-fidelity surface reconstruction across imaging modalities.