Dmitrii Khizbullin's professional page

I am a machine learning and computer vision researcher/engineer specializing in deep learning for visual perception in the fields of autonomous driving, robotics, cloud, and edge computing. I've been leading teams of up to 4 engineers, delivering highly reliable software to production, as well as providing fine-grained internal reports as a result of scientific research, pushing the state-of-the-art in computer vision and meta-learning.

I give lectures to Moscow PhysTech students (#1 tech uni in Russia), lecture about deep learning at OTUS online education, and read a course on autonomous driving at Luxoft Training. I've been mentoring and judging at circa 10 hackathons globally.

My courses

  1. I am the author of the course "LiDAR Perception for ADAS and Autonomous Driving" that I give at Luxoft and at Moscow Institute of Physics and Technology (github, Luxoft Training, my page at Luxoft)
  2. I am the author of the course "Tensor compilers for neural network training and inference" that I give at at Moscow Institute of Physics and Technology

My patents

  1. I am the primary inventor of a world-registered patent "Processing a data stream of scans containing spatial information provided by a 2d or 3d sensor configured to measure distance by using a convolutional neural network"

My articles on Medium and Hackernoon

  1. How Starship robots see the world
  2. C++17 structured bindings for more safe, functional code
  3. How to build a multi-threaded pipeline in C++ with std::async
  4. RANSAC, OLS, PCA: 3 Ways to Draw a Straight Line Across a Set of Points
  5. Variational Autoencoders (VAE): How AI Learns Whether Your Eyes Are Open Or Closed
  6. An Algorithmic Implementation of an Autonomous Driving LiDAR Perception Stack with PCL

My talks

  1. MSSTAGE 2021: How to launch neural network training on Microsoft Azure
  2. Webinar at HSE: MuZero: solving Atari with computer vision and reinforcement learning

My pet projects on GitHub

  1. Implementation of SSD bounding box detector for Kitti

  2. Interactive dataset visualization with Dash @ AWS

  3. Experimental OpenCL simulation of 256k-particle elastic relaxation

  4. Neural-network-based predictor for area of a rectangle
    extracted point cloud

  5. Verilog/FPGA driver of TOF camera for Blackfin DSP
    extracted point cloud

  6. Semantic segmentation for aeral imaging

  7. Empathetic chat bot

Materials in Russian

  1. [Youtube stream] My talk at Google DevFest Karaganda 2022 about MuZero
  2. [Youtube stream] My talk at Neconf Karaganda about metrics in machine learning
  3. [Youtube webinar] Accuracy metrics in machine learning
  4. [Youtube webinar] Transfer learning by means of interleaved training
  5. [Youtube webinar] How to start your career in Data Science
  6. [Youtube podcast] Vision of autonomous cars
  7. @bespilot
  8. My page at OTUS online education

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