Pavel Golik
Staff Research Scientist at Google DeepMind
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolPavel Golik is a Staff Research Scientist at Google DeepMind with a decade of experience advancing automatic speech recognition and acoustic modeling. He holds a PhD in Computer Science from RWTH Aachen and has led speech science teams at AppTek and research projects at RWTH, combining deep academic rigor with production-focused system administration and toolkit maintenance. Pavel has significant open-source contributions to RWTH’s RETURNn training framework and RASR toolkit, including back-end fixes for convolutional parameter handling and ASR-specific graph/logging improvements. He brings a track record of moving research into deployable systems and a knack for improving low-level tooling that quietly boosts model reliability and scalability.
10 years of coding experience
13 years of employment as a software developer
Dr. rer. nat. (PhD), Computer Science, Dr. rer. nat. (PhD), Computer Science at RWTH Aachen University
English, German, Russian, Ukrainian
Github Skills (6)
deep-learning10
python10
theano10
tensorflow9
recurrent-neural-networks9
gpu7
Programming languages (4)
Github contributions (5)
rwth-i6/returnn
Jul 2016 - Jul 2020
The RWTH extensible training framework for universal recurrent neural networks
Role in this project:
Back-end Developer
Back-end DeveloperContributions:28 commits, 4 PRs, 19 pushes in 4 years
Contributions summary:Pavel primarily contributed to the back-end aspects of the RWTH extensible training framework for universal recurrent neural networks. Their work included fixing parameter renaming issues in convolutional layers, merging branches, modifying dimension orders for ASR setup, and implementing logging and graph loading/saving. The user also made improvements to the handling of word boundaries in Sprint lattices.
rwthdeep-learninggpurecurrent-neural-networkstheano
pavelgolik/returnn
Apr 2019 - Apr 2019
The RWTH extensible training framework for universal recurrent neural networks
Contributions:3 pushes in 1 day
rwthdeep-learningrecurrent-neural-networksneural-networksmachine-learning
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