Johann Rudi
Assistant Professor at Virginia Tech
Blacksburg, Virginia, United States
Summary
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Senior🎓
Top SchoolJohann Rudi is an assistant professor at Virginia Tech with 15 years of experience developing scalable numerical methods and parallel algorithms for leadership-class supercomputers. His research bridges traditional forward modeling from his PhD with contemporary data-driven learning, inverse problems, and uncertainty quantification for complex physical systems. Previously an Argonne Scholar and Wilkinson Fellow, he has a strong track record of designing algorithms that make extreme-scale simulations tractable. Based in Blacksburg, he combines deep mathematical training (Diploma in Mathematics, PhD in Computational Science) with practical high-performance computing expertise, often focusing on where numerical analysis meets machine learning. An uncommon thread in his work is the emphasis on translating theory into parallel implementations that leverage next-generation supercomputing architectures.
16 years of coding experience
German Diploma degree (comparable to Master's degree with thesis), Mathematics, German Diploma degree (comparable to Master's degree with thesis), Mathematics at Paderborn UniversityDoctor of Philosophy - PhD, Computational Science, Engineering, and Mathematics, Doctor of Philosophy - PhD, Computational Science, Engineering, and Mathematics at The University of Texas at Austin
Github Skills (13)
pandoc7
converter7
markdown7
commonmark6
haskell6
table-of-contents5
markup-language5
markup5
petsc5
simulation4
latex2
amr1
scalable1
Programming languages (3)
Github contributions (5)
johannrudi/p4est
Sep 2015 - Mar 2023
Contributions:8 pushes, 2 branches in 7 years 7 months
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