Cuffless hemodynamic monitoring with physics-informed machine learning models
H. Crandall, T. Schuessler, F. Bělík, et al. · Nature Communications · 2026
Applied mathematics · Scientific computing
Computational methods for complex mathematical models
I am a PhD candidate in Applied Mathematics at the University of Utah, expecting to graduate in May 2027. I develop computational methods for model reduction, scientific computing, and uncertainty quantification, with applications in dynamical systems and mathematical biology.
Current work
Research at a Glance
01
Model Order Reduction
Efficient reduced models for parametric and dynamical systems.
Explore project →02
Discrete Measure Compression
Sparse approximation and robust Carathéodory pruning algorithms.
Explore project →03
Arterial Fluid Dynamics
Blood-flow and conductivity models with uncertainty quantification.
Explore project →Recent work
Selected Publications and Preprints
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Greedy Rational Approximation for Frequency-Domain Model Reduction of Parametric LTI Systems
F. Bělík, Y. Chen, and A. Narayan · 2025
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Efficient and Robust Carathéodory-Steinitz Pruning of Positive Discrete Measures
F. Bělík, J. Chan, and A. Narayan · 2025
Background
Education
University of Utah
PhD in Mathematics · 2022–present
Gustavus Adolphus College
BA Honors Mathematics and BA Computer Science · 2018–2022
Connect
Contact
- belik@math.utah.edu
- Office
- LCB 302
Beyond mathematics
Other interests
Outside my studies, I enjoy running, hiking, tennis, biking, snowboarding, music, and board games. I also enjoy coding projects, particularly in the Julia programming language.
