Andrei Anufriev
AI Research Engineer Scientist at Intel Corporation
Taufkirchen, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolAndrei Anufriev is an AI research engineer and scientist with five years of focused experience in machine learning and a decade-plus background in computer vision and 3D reconstruction. Based in Taufkirchen, Bavaria, he has been developing and integrating production-grade ML models at Intel since 2016, evolving from ADAS-focused vision work to research-driven TTS and model integration projects. His open source contributions include enhancements to OpenVINO’s open_model_zoo—notably forward-tacotron and WaveRNN demo improvements—highlighting expertise in model interfaces, multi-speaker TTS configurations, and evaluation pipelines. Trained in applied mathematics and computer science, he combines strong theoretical grounding with practical system-level engineering across perception and speech domains. Colleagues describe him as a pragmatic problem-solver who steadily turns research prototypes into reliable demos and tools for real-world deployment.
5 years of coding experience
5 years of employment as a software developer
Специалист, Прикладная математика, 4.6, Специалист, Прикладная математика, 4.6 at Сыктывкарский Государственный УниверситетMaster's degree, Angewandte Mathematik und Informatik, Master's degree, Angewandte Mathematik und Informatik at Staatliche Pitirim-Sorokin-Universität Syktywkar
German, English, Russian, komi
Github Skills (6)
inference10
deep-learning10
python10
openvino9
modello8
cnn-model7
Programming languages (1)
Github contributions (5)
openvinotoolkit/open_model_zoo
Aug 2020 - May 2021
Pre-trained Deep Learning models and demos (high quality and extremely fast)
Role in this project:
ML Engineer
ML EngineerContributions:48 reviews, 16 commits, 13 PRs in 8 months
Contributions summary:Andrei primarily contributed to the forward-tacotron text-to-speech demo, modifying existing models and demo scripts related to the forward tacotron and wavernn models. They updated model implementations, modified time formatting, and applied suggestions from code reviews. Their work included changes to model interfaces, evaluation pipelines, and incorporating multi-speaker TTS configurations within the accuracy checker, demonstrating their focus on model integration and improvements.
modelonnx-modelsdeep-learning-modelsmodel-zoopytorch-models
a-a-egorovich/open_model_zoo
Feb 2022 - Jun 2022
Pre-trained Deep Learning models and demos (high quality and extremely fast)
Contributions:15 pushes, 5 branches in 4 months
pytorchdeep-learningpre-trainedmachine-learninghigh-quality
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