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Omid Sadeghnezhad Machine Learning Engineer
[email protected]
No. 24 - 20th Tavakoli Alley - 2nd Janabz St. - Ferdowsi Blvd - Mashhad - Iran
Iranian
03/02/1997
Profile

Experienced in machine learning engineering and deep neural network. I studied Master's Degree in AI & Robotics at the K.N.Toosi University of Technology. Throughout my career, I have gained valuable experience in developing ML models, implementing video and image processing pipelines, and software developments. I thrive in collaborative environments and am committed to continually developing my skills and staying up-to-date with the latest industry developments. With a passion for better human life with AI, I am driven to make a positive impact in my field and am excited to take on new challenges and opportunities. Currently, working on object detection and tracking applications, image processing, and camera streaming pipelines. Advanced in Python programming language and Have skills with C++. I am also interested in Generative and Diffusion models.

Research Interests
  • •Generative Adversarial Networks and Diffusion models especially in computer vision and image processing fields.
  • •Object Detection and Tracking models and algorithms
  • •Image processing and computer vision
  • •Signal processing and analyzing.
    • Education
    2020 – 2023Tehran, Iran
    2015 – 2020Shahrood, Iran

    Shahrood University of Technology

    Bachelor's Degree

    Electrical and Electronics Engineering

    Courses
    Neural Networks and Deep Learning
    Advanced Information Retrieval
    Machine Learning
    Statistical Pattern Recognition
    Advanced Data Mining
    Introduction to Machine Learning in Production
    Professional Experience
    2022 – presentMashhad, Iran
    2021 – 2022Mashhad, Iran
    Programming Languages
    Python
    C++
    SQL
    Skills
    Multi Processing and Threading Programming
    Data Transferring with SocketIO
    Video Streaming and Create Pipeline with Gstreamer
    Version Controling with Git
    Managing Shared Memory in Python
    Task Queuing and Management with Celery
    Deep Neural Networks Implementation with PyTorch
    Implementing API with FastAPI
    Image and Video Processing with OpenCV
    Docker Container for DevOps
    Research Experience
  • •GAN Developments survey and Analyze the Latent space. (Basic GAN, DCGAN ACGAN, WGAN, BigGAN, PGGAN, STYLEGAN, STYLEGAN2)
  • •Evolutionary Algorithms and implementing Symbiotic Organisms Search algorithm (SOS)
    • Projects
    2022 – present

    Parstech Video Intelligent Assistant

    Intelligent Security Assistant

    AI assistance to process and analyze videos with modules like human detection, face recognition, and license plate recognition. I deeply worked on human detection and restriction area application, Camera handling and streaming, DevOps, and microservices of this application.

  • •Human detection and tracking
  • •Video Processing with OpenCV
  • •FastAPI implementation for APIs
  • •Gstreamer and OpenCV cores for the Camera management service
  • •Task queueing and workflow management with Celery
  • •Containerize services
  • •In-Memory data transfer with Redis and python shared memory
  • •Multi-Processing on the Camera management module
    • 2022

    LipReading model on Persian Dataset

    Train a LipReading model on the Persian dataset.

    2021

    Personal Protective Equipment (PPE)

    Implementation of a system to identify personal safety equipment

    A program to identify workers' safety equipment in workshop and construction environments, such as gloves, helmets, glasses, masks, safety vests, warning capabilities for people who enter prohibited areas, fire detection, and identification of work tools.

  • •Object detection models to localize humans
  • •Human tracking
  • •Pose estimation to check human body status
  • •Body part localizer for head, hands, and chest
  • •Equipment classifiers models
  • •Pipeline implementation to estimate human fall
  • •Models were implemented in Pytorch and scikit-learn packages.
  • •Faster functions with numba jit compiler
    • 2021

    Pressure Ulcer

    Analysis of pressure sensor data of hospital beds for prediction and diagnosis of bed sores.

  • •Body Segmentation Models.
  • •Pose estimation model for pressure sensor data
  • •Video processing with OpenCV
  • •Signal Capturing from human poses
    • 2021

    Cardiac Medical Data Augmentation

    Train ACGAN on real Cardiac Medical Data in order to generate fake data for augmentation.

  • •Pytorch Implementation
  • •Tabular data management with Pandas python package.
    • 2021

    Human localization

    In two categories of foreground and background images, it takes a personal image from the foreground, and in order to place it in the background image, it first finds the right place then masks the image and places it in the background, and then corrects the color and light also improves the image by a model.

  • •Depth Estimation
  • •Image Gradient and B-Spline methods for image brightness correction
  • •Color correction models
  • •Object detection models to find humans and other objects in the Image.
  • •Image Perspective calculation
  • •Image blending
    • 2021

    OCR

    Optical Character Recognition on German administrative forms.

  • •Use tesseract and self-trained text recognition neural network.
  • •Use Clustering algorithms to find lines.
  • •Use OpenCV and Image processing methods to find information blocks.
    • Languages
    Persian
    English
    Interests
    Guitar
    Nature Lover
    Video Game
    Psychology & Philosophy
    Ping Pong