Mikhail Petrushkov

Mikhail Petrushkov

Software Engineer

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  • Timeline

  • About me

    AI Algorithm Engineer

  • Education

    • Nizhniy Novgorod State Technical University named after R.Y. Alekseev (NSTU)

      2013 - 2015
      Master's degree Radar Communications and Systems Technology
    • Nizhniy Novgorod State Technical University named after R.Y. Alekseev (NSTU)

      2009 - 2013
      Bachelor's degree Radar Communications and Systems Technology
  • Experience

    • Itseez

      Jul 2015 - Nov 2015
      Software Engineer

      • Developed and optimized OpenVX extensions, resulting in significant speedimprovements of computer vision algorithms through efficient image processing oncustomer hardware.

    • Intel Corporation

      Feb 2017 - Oct 2018
      Software Engineer

      • Collaborated on the development, validation and bug fixing of an Intel cross-platformlibrary for developing media applications on Windows and Embedded Linux(MediaSDK), specifically for video encoding, decoding and video/image processing.• Successfully resolved critical customer issues.• Provided internal training to empower team members with the necessary knowledge.

    • IntelliVision

      Oct 2018 - Dec 2019
      R&D Engineer

      • Explored and implemented deep learning methods for object detection andclassification, including datasets preparation, model training and evaluation.• Optimized deep learning models for integration on embedded hardware, focusing ongraph optimization and model quantization.

    • Luxoft

      Jan 2020 - Jan 2021
      Software Engineer

      • Developed deep learning algorithms for efficient post-processing in the renderingpipeline.• Evaluated various methods for denoising Monte Carlo rendering results.• Contributed to the development of a cross-platform inference engine forGPU-accelerated inference of neural network models.

    • Intel Corporation

      Jan 2021 - now

      • Conducted research on deep learning algorithms for real-time image enhancement in 3D rendering pipelines, focusing on anti-aliasing and super-resolution.• Enhanced ML R&D infrastructure by developing a distributed training and evaluation framework; implemented a distributed data generation pipeline, reducing the time required for setting up the data generation by over 70%.• Improved training speed by 40% through custom Pytorch operations in CUDA.

      • AI Algorithm Engineer

        Aug 2022 - now
      • Deep Learning Software Engineer

        Jan 2021 - Aug 2022
  • Licenses & Certifications

    • Интерактивный тренажер по SQL

      Stepic.org
      Oct 2020
      View certificate certificate
    • SQL for Data Science

      Coursera
      Oct 2020
      View certificate certificate
    • Build Better Generative Adversarial Networks (GANs)

      Deeplearning.ai
      Oct 2020
      View certificate certificate
    • Machine Learning by Stanford University

      Coursera
      Jan 2017
      View certificate certificate
    • Convolutional Neural Networks by deeplearning.ai

      Coursera
      Sept 2018
      View certificate certificate
    • Build Basic Generative Adversarial Networks (GANs)

      Deeplearning.ai
      Oct 2020
      View certificate certificate
    • Sequence Models by deeplearning.ai

      Coursera
      Oct 2019
      View certificate certificate
    • Structuring Machine Learning Projects by deeplearning.ai

      Coursera
      Sept 2018
      View certificate certificate
    • Neural Networks and Deep Learning by deeplearning.ai

      Coursera
      Sept 2018
      View certificate certificate
    • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization by deeplearning.ai

      Coursera
      Sept 2018
      View certificate certificate