Evgeny Tsykunov
Performance Engineer at NVIDIA
Munich, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolEvgeny Tsykunov is a performance-focused software engineer with a decade of experience building and optimizing ML systems across academia, startups, and big tech. He holds a PhD in Robotics and has four years at Intel developing low-precision quantization tooling and model-analysis features that helped drive OpenVINO to millions of downloads. As a technical founder/CTO he shipped a hardware–software warehouse digitization product that reached $200K revenue without outside funding, and he continues to blend product-first pragmatism with research rigor. Now at NVIDIA, he focuses on squeezing latency and accuracy from deep learning stacks, drawing on hands-on contributions to projects like NNCF (YOLOv4 support, compression losses, and stability fixes). Known for tackling hard systems-to-model gaps, he pairs strong publication credentials (H-index 9) with practical productionization skills.
10 years of coding experience
10 years of employment as a software developer
Mechatronics, Robotics, and Automation Engineering, Mechatronics, Robotics, and Automation Engineering at Massachusetts Institute of TechnologyDoctor of Philosophy - PhD, Engineering Systems (Space CREI, Intelligent Space Robotics Laboratory), Doctor of Philosophy - PhD, Engineering Systems (Space CREI, Intelligent Space Robotics Laboratory) at Skolkovo Institute of Science and TechnologyMaster of Science (M.S.), Power Engineering, GPA 98.4% (with honors), Master of Science (M.S.), Power Engineering, GPA 98.4% (with honors) at Bauman Moscow State Technical University
English, Russian
Github Skills (11)
model-compression10
object-detection10
tensorflow10
yolov410
deep-learning9
quantization9
computer-vision8
pytorch8
trainings7
modeling7
sparse7
Programming languages (6)
Github contributions (5)
openvinotoolkit/nncf
Mar 2021 - Jun 2022
Neural Network Compression Framework for enhanced OpenVINO™ inference
Role in this project:
ML Engineer
ML EngineerContributions:157 reviews, 35 commits, 33 PRs in 1 year 3 months
Contributions summary:Evgeny primarily contributed to the implementation and support of YOLOv4 within the NNCF framework. This includes the addition of functional support for YOLOv4, modifications to preprocessing and loss functions, and integration of evaluation components. Furthermore, the user was involved in adding compression loss and resolving potential issues with model training, such as loss instability and handling batch sizes in tests.
bertsemantic-segmentationmixed-precision-trainingtensorflowclassification
negvet/training_extensions
Nov 2022 - Jan 2025
Trainable models and NN optimization tools
Contributions:92 pushes, 57 branches in 2 years 3 months
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