Cengiz Cakir
Sariyer, Istanbul, Türkiye Turkey
Summary
🤩
Rockstar🎓
Top SchoolCengiz Çakır is an electronics engineer with 11 years of hands-on experience in embedded systems, digital design, and data analysis, currently based in Istanbul. He has built real-time signal processing pipelines on Zynq SoCs and implemented RF DSP modules in VHDL, pairing embedded software with custom FPGA accelerators. His research and industry experience spans wearable sensors, EEG/ECoG analysis for motor imagery, and an active interest in brain-computer interfaces that bridges neuroscience and engineering. Cengiz has contributed to prominent open-source projects like TensorFlow Graphics, adding texture mapping, mipmapping and dual-quaternion functionality to enable differentiable graphics workflows. He combines academic roles (affiliated with Koç Üniversitesi and connections to Cambridge/Google research communities) with practical product-focused engineering, making him adept at moving ideas from prototype to deployable systems. Notably, his background includes both low-level driver development and high-level data analysis, giving him fluency across the hardware–software stack.
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 (7)
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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