Fredrik Johansson is a research scientist and software developer with 19 years’ experience specializing in computational mathematics, computer algebra, and high-precision arithmetic. He is the creator of mpmath and a principal author of Arb, and has co-authored FLINT while contributing to SymPy, SageMath and Nemo.jl, blending deep theory with production-quality C and Python libraries. His work spans new algorithms for rigorous numerical integration, efficient matrix and power-series arithmetic, and special-function computation, with a strong emphasis on correctness and rigorous testing. Based in Bordeaux, he combines a PhD in mathematics with an engineering physics background to bridge research and implementation at scale. Notably, his implementations have advanced practical arbitrary-precision tools used across open-source mathematical ecosystems.
18 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Mathematics, Doctor of Philosophy - PhD, Mathematics at Johannes Kepler Universität Linz
Master of Science - MS, Engineering Physics, Master of Science - MS, Engineering Physics at Chalmers University of Technology
Arb has been merged into FLINT -- use https://github.com/flintlib/flint/ instead
Role in this project:
Back-end Developer
Contributions:14 reviews, 1999 commits, 156 PRs in 10 years 11 months
Contributions summary:Fredrik implemented inverse Weierstrass elliptic functions and lattice invariants to enhance the capabilities of the FLINT library's arbitrary precision arithmetic functions. Their contributions involved modifying existing documentation and test code to incorporate the new functionality. The changes include implementing new functions for calculations, and the integration of these features within the existing structure of the project.
Contributions:1 release, 1019 commits, 92 PRs in 15 years 6 months
Contributions summary:Fredrik made several contributions to a Python library for arbitrary-precision floating-point arithmetic. Their work primarily involved implementing and improving core mathematical functions, including the Lerch transcendent, the von Mangoldt function, and the Riemann-Siegel Z and theta functions. Furthermore, the user focused on optimizing existing numerical methods by implementing the Abel-Plana summation formula for divergent series and by enhancing the accuracy of existing functions such as the Bessel and Airy functions.
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