Daniel Polak
Senior Staff Scientist at Siemens Healthineers
Erlangen, Bavaria, Germany
Summary
👤
Senior🎓
Top SchoolDaniel Polak is a Senior Staff Scientist with 11 years of experience at the intersection of MR physics, AI-driven image reconstruction, and translational industry–academia collaboration. Based in Erlangen, he progressed from doctoral research at Heidelberg to applied roles and leadership at Siemens Healthineers, including visiting research stints at MIT and Harvard Medical School. He combines deep physics training with hands-on software development—contributing C implementations to the widely used BART MRI toolbox for noise estimation and sensitivity-map improvements. His work bridges algorithmic innovation (motion correction and reconstruction) with product-oriented application development, enabling clinical-grade imaging solutions. Comfortable moving between research prototypes and production code, he brings a rare mix of academic rigour and pragmatic engineering to medical imaging problems.
11 years of coding experience
Technischen Universität DarmstadtDoktor (Ph.D.) Physics, Doktor (Ph.D.) Physics at Heidelberg University
Github Skills (6)
c1710
mri10
c1110
numerical-methods9
algorithms9
deep-learning7
Programming languages (5)
Github contributions (5)
mrirecon/bart
Nov 2015 - Feb 2021
BART: Toolbox for Computational Magnetic Resonance Imaging
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 review, 79 commits, 47 PRs in 5 years 3 months
Contributions summary:Daniel primarily focused on implementing a noise estimation command (`estvar`) and its related functions within the `bart` toolbox. This involved writing C code to simulate and analyze noise characteristics within the context of computational magnetic resonance imaging (MRI). Further contributions included the development of soft-weighting and thresholding algorithms for improving the accuracy of sensitivity maps in the reconstruction pipeline.
magneticresonancedeep-learningbart-toolboxcompressed-sensing
sidward/sigpy
Apr 2019 - Aug 2021
Python package for signal processing, with emphasis on iterative methods
Contributions:67 pushes, 6 branches in 2 years 4 months
signalpythoniterativeiterative-methodssignal-processing
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