Simon Dirmeier
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolSimon Dirmeier is an ML and research engineer based in Zurich with 10 years of experience applying probabilistic modeling and numerical methods to real-world problems. He holds an MSc from TUM and a Doctor of Science from ETH Zürich, blending strong academic rigor with production-focused engineering. At the Swiss Data Science Center and in open-source work, he contributes to core probabilistic tooling—evidenced by his additions to the Stan Math Library, where he implemented Poisson-binomial PMF/CDF/CCDF and RNG support while fixing dimensioning and numerical issues. Comfortable in C++ and automatic differentiation frameworks, he focuses on reliable, high-performance implementations for statistical computation. Colleagues describe him as a methodical problem-solver who translates complex mathematical ideas into robust, usable code.
10 years of coding experience
Bachelor of Science, Bachelor of Science at Technical University MunichDoctor of Science, Doctor of Science at ETH Zürich
English, German
Github Skills (16)
data-modeling10
math-functions10
automatic-differentiation10
c-language10
probabilistic-programming10
statistical-models10
cprogramming-language10
probabilistic-reasoning10
maths10
probabilistic-models10
mathlib9
math-library9
math9
stan9
mathematics9
Programming languages (15)
Github contributions (5)
stan-dev/math
Jun 2020 - Apr 2021
The Stan Math Library is a C++ template library for automatic differentiation of any order using forward, reverse, and mixed modes. It includes a range of built-in functions for probabilistic modeling, linear algebra, and equation solving.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 reviews, 53 commits, 4 PRs in 10 months
Contributions summary:Simon implemented the Poisson-binomial log probability mass function (PMF) along with its corresponding log cumulative density function (CDF), and log complementary cumulative density function (CCDF) functions. These contributions involved the creation of new header files and the integration of these functions within the Stan Math Library, a C++ template library for automatic differentiation. Furthermore, the user addressed dimensioning issues, fixed code related to computation, and added the functionality for random number generation (RNG) for the Poisson-binomial distribution, thus improving the library's capabilities in probabilistic modeling.
automatic-differentiationprobabilistic-modelingsundialsmodestemplate-library
ramsey-devs/ramsey
Dec 2021 - Jan 2023
Probabilistic deep learning using JAX
Contributions:6 releases, 1 review, 44 commits in 1 year 1 month
pythondeep-learningbayesian-inferencehaikuneural-networks
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