Tomasz Kuśmierczyk: About me
I am a machine learning researcher working on probabilistic modeling, Bayesian deep learning, and reliable AI systems. I am interested in bridging statistical inference and modern deep learning to develop models that are more robust, interpretable, and effective in real-world settings.
My recent work is on uncertainty quantification for large language models: making Bayesian treatment of large models affordable through parameter-efficient adapters (Findings of EMNLP 2025), and distilling expensive uncertainty-aware teachers into models that need a single forward pass (ECML-PKDD 2026). From 2024 to 2026 I led the NCN/MSCA project Balancing priors and learning biases to improve Bayesian Neural Networks at the Jagiellonian University, as principal investigator. Earlier I was a postdoctoral researcher at the University of Helsinki, and I hold a PhD from NTNU Trondheim.
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