Ilya Trushkin

Moscow, Moscow City, Russia

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Summary

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Rockstar

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Top School

Ilya Trushkin is an AI frameworks engineer with five years of experience building and optimizing machine learning systems across research and production at Intel, Sberbank, and security-focused startups. He combines a strong mathematical background (MS in Data Mining) with hands-on expertise in PyTorch, TensorFlow, OpenVINO and federated learning, having improved tumor segmentation accuracy by 25% and accelerated Stable Diffusion inference with OpenVINO. Ilya has contributed to widely used open-source projects—helping maintain OpenFL and enhancing OpenVINO notebooks—fixing dataset issues and implementing FedAvg/FedProx and model conversion pipelines. At Sberbank he helped raise text-detection F1 to 90.7% for Kandinsky text-to-image/video work, and previously developed real-time anomaly and under-vehicle detection systems for live deployments. Known for a meticulous, analytical approach, he thrives in cross-functional, global teams and prefers tackling robustness and deployment gaps that researchers often overlook. Based in Moscow, he blends research-driven curiosity with pragmatic engineering to turn state-of-the-art methods into reliable products.

5 years of coding experience

7 years of employment as a software developer

Bachelor of Science - BS Applied Mathematics, Bachelor of Science - BS Applied Mathematics at Izhevsk State Technical University (ISTU)

Bachelor's degree Mathematics, Bachelor's degree Mathematics at Ural State University named after A.M.Gorky

Master of Science - MS Data Mining, Master of Science - MS Data Mining at Higher School of Economics

Русский, English

Github Skills (14)

mask-rcnn10

faster-rcnn10

pytorch10

machine-learning10

inference10

deep-learning10

tensorflow10

python10

federated-learning10

mnist10

openvino10

model-optimization10

keras9

computer-vision9

Programming languages (2)

Jupyter NotebookPython

Github contributions (5)

securefederatedai/openfl

Feb 2021 - Jan 2023

An Open Framework for Federated Learning.

Role in this project:

ML Engineer

Contributions:113 reviews, 174 commits, 58 PRs in 1 year 11 months

Contributions summary:Ilya's contributions primarily involve cleaning and updating Jupyter notebooks related to a PyTorch CNN histology tutorial and the MNIST dataset. They address issues with downloading the MNIST dataset and implement fixes to resolve 503 service unavailable errors. The commits also include adding functionality to TensorFlow native API, specifically implementing a Federated Averaging (FedProx) algorithm for Keras and updating the PyTorch implementation.

federated-analyticspythonsecure-computationfedavgfedopt

openvinotoolkit/openvino_notebooks

Mar 2023 - Apr 2024

📚 Jupyter notebook tutorials for OpenVINO™

Role in this project:

ML Engineer

Contributions:84 reviews, 35 PRs, 141 comments in 1 year 1 month

Contributions summary:Ilya primarily worked on improving and converting machine learning models within the repository. Their contributions include using the Python API to convert models, modifying the code to use the Model Optimizer Python API, and integrating the NNCF library for post-training quantization. These changes suggest a focus on optimizing and integrating pre-trained models. The commits also include the addition and modification of example notebooks demonstrating the application of the converted models.

deep-learningjupyter-notebooknotebookinferencecomputer-vision

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