Summary
Yagiz Olmez is a PhD-qualified mechanical engineer and software-savvy researcher with six years of experience building ML, control, and simulation tools that bridge theory and production. He has shipped C++ systems at Intel that reduced curvature violations in OPC masks by 92% and built physics-informed ML pipelines at Brunswick that learned boat dynamics from just eight short recordings. His research at UIUC produced a TensorFlow PDE solver for particle filters with 5x speedups and novel state-estimation approaches for epidemic modeling and neural decoding, leading to four first-authored papers and conference presentations. Comfortable across Python, C++, MATLAB/Simulink, Git, and Linux, he pairs rigorous mathematical foundations with practical profiling and optimization skills (VTune). Based in the San Francisco Bay Area, he’s actively pursuing full-time roles where software, AI, or control systems meet measurable real-world impact. An under-the-radar strength is his track record of translating complex stochastic control and signal-processing research into deployable code that stakeholders — including CEOs — can act on.
6 years of coding experience
6 years of employment as a software developer
Almanca, Almanca at Bornova Anadolu Lisesi
University of Illinois Urbana-Champaign
Summa Cum Laude, Mechanical Engineering, Summa Cum Laude, Mechanical Engineering at Bilkent Üniversitesi
Exchange Student, Mechanical Engineering, Exchange Student, Mechanical Engineering at National University of Singapore
Turkish, İngilizce, German