Nikolay Bogdanov
DevOps Lead, Technology Architect ADAS AND Autonomus Vehicle AI at Autoliv -> Veoneer -> Arriver -> Qualcomm
Wang, Bavaria, Germany
Summary
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Rockstar🎓
Top SchoolNikolay Bogdanov is a DevOps Lead and Technology Architect specializing in ADAS and autonomous vehicle AI with nine years of hands-on experience across automotive Tier-1s and OEM projects. He has driven system and software development for safety-critical features like Forward Collision Avoidance, Adaptive Cruise Control, Traffic Assist and motion planning, and now leads DevOps and architecture at Wipro focused on autonomous systems. His background in mechatronics from TU Berlin and early work on vehicle dynamics and functional safety underpin a pragmatic, systems-oriented approach to building reliable, real-time software. Nikolay also contributes to scientific open-source projects—enhancing MCSCF methods in the notable PySCF quantum chemistry library—illustrating a rare blend of automotive control expertise and numerical computing skill. Colleagues value him for translating complex safety requirements into testable, production-grade features and for bridging low-level algorithms with DevOps-driven deployment. Based in Wang, Bavaria, he combines automotive domain depth with a curiosity for advanced computational methods that improve algorithmic robustness.
9 years of coding experience
Diplom-Ingenieur, Informationstechnik im Maschinenwesen (Mechatronik), Diplom-Ingenieur, Informationstechnik im Maschinenwesen (Mechatronik) at Technische Universität Berlin
German, English
Github Skills (8)
quantum-chemistry10
computational-chemistry10
python10
numerical-analysis8
numerics8
numerical-computing8
algebra8
numerical-methods8
Programming languages (4)
Github contributions (5)
pyscf/pyscf
May 2018 - Nov 2022
Python module for quantum chemistry
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 reviews, 60 commits, 32 PRs in 4 years 7 months
Contributions summary:Nikolay primarily contributed to the PySCF library by modifying and enhancing the implementation of the multi-configurational self-consistent field (MCSCF) methods, specifically related to the analysis and canonicalization of molecular orbitals. The contributions include incorporating the `with_meta_lowdin` option for Mulliken analysis and canonicalization procedures, adding and modifying functions like `cas_natorb` and `canonicalize`. Further changes involved improvements to the Density Matrix Renormalization Group (DMRG) code and file reading for the IC-MP2 method.
pythonchemistryquantum-computingpython-modulequantum-chemistry
bogdanoff/StackBlock
Oct 2019 - Mar 2024
Contributions:5 pushes, 3 branches in 4 years 5 months
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