Jan Jelínek

Amsterdam, NL • student MSc Artificial Intelligence, University of Amsterdam

About

Master’s student in Artificial Intelligence with a strong background in Applied Mathematics and deep learning research. Experienced in developing computer vision models for agricultural robotics and implementing model validation tests in professional banking environments. Skilled in Python and Java with a focus on creating interpretable AI solutions for real-world problems.

Education

Sept 2025 – July 2027

MSc Artificial Intelligence

University of Amsterdam (UvA)
Sept 2021 – June 2025

BSc Applied Mathematics (incl. Minor in Deep Learning)

Inholland University of Applied Sciences

Publications & Selected Projects

Mechanistic Interpretability Reproducibility & Extension
Hamid Reza Ahmadi*, Mihnea Andrei Bârsan*, Jan Jelínek*, Robert-Ștefan Sofroni* • Transactions on Machine Learning Research (TMLR), 2026
[PDF] [OpenReview]
* Equal contribution
Reproducing Generative Recommendation with Semantic IDs
University of Amsterdam • June 2026
  • Implemented and extended a generative recommender framework leveraging semantic IDs to improve recommendation accuracy.
Solving the Laplace Equations using residual PINNs
University of Amsterdam • Apr 2026 – May 2026
  • Investigated harmonic decomposition combined with a residual PINN network to solve the Laplace Equation.

Experience

Feb 2025 – June 2025

Artificial Intelligence Intern Researcher

Biomimicry Research Group – Inholland (Alkmaar, NL)
  • Developed a Deep Learning model to detect pests and predators at various life stages (larvae, adults) to facilitate pesticide-free farming.
  • Conducted experiments to collect data which was then integrated into the model.
Jan 2024 – Aug 2025

Coach – Minor Understanding AI

Inholland University of Applied Sciences (Amsterdam, NL)
  • Served as a teacher’s assistant and coach, guiding students through foundational AI concepts and practical implementations.
Sept 2023 – Jan 2024

Model Validation Intern

Nationale-Nederlanden (Den Haag, NL)
  • Implemented A-IRB validation tests using Python, specifically focusing on Probability of Default (PD) metrics.

Skills & Languages

Programming
Python, Java, MySQL, NoSQL
Machine Learning
Deep Learning, NLP, Computer Vision, Statistics, Calculus, GIS
Languages
Czech (Native), English (Fluent), Slovak (Professional Fluency), German (B1), Dutch (Conversational)