Summary
Arash Golmohammadi is a doctoral researcher in Göttingen with eight years of cross-disciplinary experience applying computational science, physics, and mechanical engineering to real-world problems. His work spans biological neural network modeling, deep brain stimulation, drone navigation without GPS, and astrophysical spectral analysis, reflecting a strong blend of statistical/machine learning modeling and physics-informed simulation. He emphasizes interpretable data visualizations and optimization to support decision-making, and has taken models from simulation to live evaluation, including algorithmic trading experiments on the NYSE. Comfortable bridging academic research and applied engineering, he brings both teaching experience and hands-on dataset construction (CFD-derived) to make complex systems tractable. An unusual thread through his career is leveraging domain knowledge—from celestial mechanics to neurodynamics—to design ML solutions that respect underlying physical structure.
8 years of coding experience
3 years of employment as a software developer
Astronomy and Astrophysics Olympiad, Gold medalist, Astronomy and Astrophysics Olympiad, Gold medalist at Young Scholars Club
High School, Mathematics and Physics, High School, Mathematics and Physics at Salam Dibaji
Master's degree, Computational Science, Master's degree, Computational Science at EPFL (École polytechnique fédérale de Lausanne)
Bachelor's degree, Physics, Bachelor's degree, Physics at Sharif University of Technology
English, Persian, French, German