Cengiz Cakir
Sariyer, Istanbul, Türkiye Turkey
Summary
🤩
Rockstar🎓
Top SchoolCengiz Cakir is an electronics engineer with 10 years of experience specializing in embedded systems, FPGA/SoC design, digital design and data analysis, with a growing focus on brain-computer interfaces. He designed and implemented a multi-core processing pipeline that marries embedded software with a custom accelerator in programmable logic and has developed drivers and an NFC-enabled mobile app for capacitance-to-digital AFE ICs. An active open-source contributor to the well-known TensorFlow Graphics project, he added texture-mapping, mipmapping and dual-quaternion functionality to improve differentiable graphics capabilities. He also has hands-on neuroscience training from Neuromatch Academy and performed ECoG-based brain connectivity analysis, blending hardware skills with neural data science. Based in Istanbul, he moves comfortably between national research labs, industry internships and product-focused engineering, uniquely bridging low-level firmware and FPGA acceleration with machine learning and differentiable graphics work.
10 years of coding experience
2 years of employment as a software developer
High School Diploma, High School Diploma at Denizli Erbakır Fen LisesiBachelor of Science - BS, Electronics and Communication Engineering, Bachelor of Science - BS, Electronics and Communication Engineering at Istanbul Technical University
Turkish, İngilizce, French
Github Skills (8)
tensorflow210
tensorflow10
python10
geometry9
linear-algebra9
3d-graphics9
rendering8
render8
Programming languages (1)
Github contributions (5)
tensorflow/graphics
Jan 2021 - Feb 2022
TensorFlow Graphics: Differentiable Graphics Layers for TensorFlow
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 reviews, 45 commits, 3 PRs in 1 year
Contributions summary:Cengiz primarily contributed to the TensorFlow Graphics library by adding and modifying modules. Their work included adding missing modules to initialization files, starting a texture package with texture mapping implementation using bilinear interpolation, and adding mipmapping capabilities to the rendering texture package. Furthermore, they added multiple functionalities to the dual_quaternion module, including the functions conjugate, inverse, norm, and the ability to transform between different dual quaternion representations. These changes enhance the library's capabilities for differentiable graphics applications.
differentiablecomputer-graphicslayerstensorflowgraphics
G4G/githubtest2
Jun 2015 - Jan 2016
Contributions:2 PRs, 3 pushes, 4 branches in 7 months
testingtest-project
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