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Dmitrii Murygin

Verified Expert in Engineering

Software Developer

Bishkek, Chuy Province, Kyrgyzstan

Toptal member since December 7, 2021

Expertise

Ubuntu C++C Deep Learning Git Python Visual Studio Development TensorFlow NLP Machine Learning PyTorch

Bio

Dmitrii is an experienced C++ developer and deep learning engineer. As a middle C++ developer he has five years of experience in research computation related projects. As a deep learning engineer he has two years of solid experience in NLP direction connected to transformer and BERT based models.

Portfolio

Innopraktika

C++, CMake, Clang, Google Test, Git, Ubuntu, Visual Studio Code (VS Code)...

Huawei Technologies Co.

Python 3, TensorFlow, PyTorch, BERT, Custom BERT, Neural Networks

Experience

C++ - 5 years
Git - 5 years
Ubuntu - 5 years
C - 4 years
Python 3 - 4 years
Google Test - 3 years
CMake - 3 years
TensorFlow - 2 years

Work Experience

Research Software Engineer

2021 - PRESENT

Innopraktika

Developed a new method of 3D image segmentation based on supervoxel clusterization.
Developed a new method of pore-network extraction based on discrete Morse theory.
Implemented three parallel algorithms for 3D image segmentation and two parallel algorithms for 3D image filtering.
Implemented a cutting-edge programming module to simulate fluid filtration in pore-network models.

Technologies: C++, CMake, Clang, Google Test, Git, Ubuntu, Visual Studio Code (VS Code), OpenMP

Developer/Researcher

2018 - 2021

Huawei Technologies Co.

Developed a BERT model for mobile devices using C++. Compared it with other BERT versions converted from the most famous frameworks.
Made some contributions to various projects such as TensorFlow, Swift, LLVM, Stardust, and TVM.
Implemented the following neural network compression techniques: quantization, distillation, and prunning.

Technologies: Python 3, TensorFlow, PyTorch, BERT, Custom BERT, Neural Networks

Project History

Mobile BERT Benchmark

A benchmark for BERT I created that is converted by ONNX for mobile devices. It is possible to get performance for tiny, base, and large versions of the BERT model. To create a mobile BERT version, it uses the ONNX Runtime tool and creates a fused BERT model.

Education

2014 - 2018

Bachelor's Degree in Mathematics and Computer Science

Moscow State University - Moscow, Russia

Skills

Libraries/APIs

TensorFlow, OpenMP, PyTorch

Tools

CMake, Git, Vim Text Editor, Open Neural Network Exchange (ONNX)

Languages

C, C++, Python 3

Platforms

Ubuntu, Visual Studio Code (VS Code)

Frameworks

Google Test

Other

Clang, Deep Learning, LLVM, Machine Learning, Natural Language Processing (NLP), BERT, Custom BERT, Neural Networks, Generative Pre-trained Transformers (GPT)

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