Iaroslav Omelianenko
Software Engineering Advisor at NewGround
Kyiv, Ukraine
Summary
🤩
Rockstar🎓
Top SchoolIaroslav Omelianenko is a seasoned software engineering advisor and CTO with over two decades of hands-on experience building mobile, cloud-native and high-performance distributed systems. He combines deep research credentials in AI—neuroevolution, genetic algorithms, reinforcement and deep learning—with practical MLOps and SDK design work at Comet, authoring Python, Java and R tooling for production ML. A full‑stack practitioner fluent in Java, Objective‑C/Swift, Go and Python, he also architects scalable microservices and cluster solutions using Docker, Kubernetes, Spark and cloud platforms (AWS, GCP, IBM). He’s an active open‑source contributor to prominent ML projects like Ultralytics YOLO, where he improved CometML integration and model visualization. Notably, he pairs curiosity-driven research (quantum computing on IBM Q, brain–computer interfaces) with product delivery, having led dozens of client projects from concept to release. Based in Kyiv, he’s equally comfortable mentoring engineering teams and rolling up his sleeves to implement complex algorithms in production.
10 years of coding experience
5 years of employment as a software developer
Master of Engineering (M.Eng.), Production management, Technological process control, Master of Engineering (M.Eng.), Production management, Technological process control at Ukrainian State University of Food TechnologiesHigh School, High School at High School #48, Kiev
English, Russian, Ukrainian
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (10)
object-detection10
computer-vision10
pytorch10
machine-learning10
deeplearning-ai10
deep-learning10
python10
macos-big-sur6
go6
macos6
Programming languages (9)
Github contributions (5)
ultralytics/ultralytics
Oct 2024 - Mar 2025
Ultralytics YOLO11 🚀
Role in this project:
ML Engineer
ML EngineerContributions:5 PRs, 2 comments in 4 months
Contributions summary:Iaroslav primarily contributed to integrating and improving CometML logging within the Ultralytics YOLO repository. Their work involved updating the Comet API, fixing issues with class name indexing, and adding segmentation support for more comprehensive model evaluation. Additionally, they made improvements to the visualization of labels within the classification task, enhancing the integration of Comet within the repository.
pytorchdeep-learningyolov8object-detectiononnx
NewGround-LLC/ai
Apr 2019 - Apr 2024
The NewGround AI lab web
Contributions:1 review, 9 PRs, 18 pushes in 5 years 1 month
deep-learningartificial-intelligencelabmachine-learning
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