Jaan Tollander de Balsch is a computational scientist with a background in computer science and applied mathematics. Professionally, he develops software packages for scientific computing using the Julia language and Git-based workflows. He values producing properly-tested and documented software packages with clean APIs that other developers can understand and use. He has applied these skills for developing software packages for simulation algorithms and mathematical optimization models and running code on high-performance computing environments.
MSc in Computer Science, 2018 - present
BSc in Applied Mathematics, 2014 - 2018
At Gamma-Opt, a research group that also a part of the System Analysis Laboratory, I worked at developing software packages for mathematical optimization models using the Julia language with JuMP modeling library. The models were based on earlier research produced by the other research group members. In addition to writing the source code, the development process included writing detailed documentation, unit tests, and configuring the packages. We used Git-based workflows, such as GitHub Actions, to automatically run unit tests and deploy documentation and GitHub Pages for hosting the documentation.
I also helped to set up the Gamma-Opt website and GitHub team and worked as a course assistant for the “Introduction to Optimization” teaching demo exercises over Zoom.
Crowd dynamics studies the movement of crowds of humans people. My work focused on developing software for simulating crowd dynamics based on existing research results. We implemented the simulation using the Python programming language and its numerical computing packages. I wrote an extensive article about implementing the simulation mechanics, which you can read on my website at How to Implement Continuous-Time Multi-Agent Crowd Simulation. All software code is available at the github/crowddynamics.
Strongest skills are in bold. Names are in italics.
Julia, Python, Bash, C++, C, Haskell, Scala, Markdown, LaTeX
Algorithm design, Data structures, Scientific computing, Theory of computation
Mathematical modeling, optimization, JuMP.jl, Numerical analysis, Computer algebra
PyTorch, R, Stan, Deep learning, Bayesian statistics
Git, GitHub, Git-based workflows, DevOps, JAMstack, Hugo
Linux, Ubuntu, VSCode, Atom, JetBrains, Jupyter