CV
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General Information
Full Name | Omid Sadeghnezhad |
Date of Birth | 2nd February 1997 |
Languages | Reading, writing and speaking competencies for English, Native Persian. |
Research Interests
- Generative Adversarial Networks and Diffusion models especially in computer vision and image processing fields
- Object Detection and Tracking models
- Image processing and computer vision
- Signal processing and analyzing
Research Experience
- Evolutionary Algorithms and implementing Symbiotic Organisms Search algorithm (SOS) - 2023
- GAN Developments survey and Analyze the Latent space. (Basic GAN, DCGAN ACGAN, WGAN, BigGAN, PGGAN, STYLEGAN, STYLEGAN2, StarGAN, SEAN) - 2022
- Theories of Multimodal deep learning - 2021
- Clothes virtual try-on models survey. (ACGPN, SwapNet, CP-VTON-PLUS) - 2020
- Image harmonization and blending methods (Dovenet, DeepImageBlending), Brightness transfer (global and local transfer), color transfer methods (mean-std transfer, Lab mean transfer, and pdf transform), and color constancy with image to image translation (pix2pix, CycleGAN, and contrastive-unpaired-translation) - 2020
- Image depth extractions (monoDepth, Pydnet) and salient object detection models (Basnet, U2Net, PoolNet, CPD) - 2020
Education
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2020-2023 Master
K. N. Toosi University of Technology - Artificial Intelligence.
- Seminar
- Generative Adversarial Networks (GAN) Survey
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2015-2020 Bachelor
Shahrood University of Technology - Electrical and Electronics Engineering
Experience
-
2021-present Machine Learning Engineer
Parstech.Co - Description 1.
- Description 2.
- Description 3.
- Sub-description 1.
- Sub-description 2.
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2020-2021 Machine Learning Engineer
TaraTech.Co - Description 1.
- Description 2.
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2019-2020 Internship
Telecommunication Company of Iran
Skills
Coding | |
Python, sql, C, C++, LATEX | |
Databases | |
MYSQL, SQlite, Redis | |
ML Frameworks | |
Pytorch, Scikit-Learn | |
Misc. | |
Multi Processing and Threading Programming , Managing Shared Memory in Python , Implementing API with FastAPI , Data Transferring with SocketIO , Task Queuing and Management with Celery , Image and Video Processing with OpenCV , Video Streaming and Create Pipeline with Gstreamer , Deep Neural Networks Implementation with PyTorch , Docker Container for DevOps , Version Controling with Git |
Projects
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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.
- Features
- 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
- Containerized services
- In-Memory data transfer with Redis and python shared memory
- Multi-Processing and Multi-Threading features implemented
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2022 LipReading model on Persian Dataset
- Train a LipReading model on the Persian dataset.
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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.
- Features
- Human detection and 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
- Faster functions with numba jit compiler
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2021 Pressure Ulcer
- Analysis of pressure sensor data of hospital beds for prediction and diagnosis of bed sores.
- Features
- Body Segmentation Models.
- Pose estimation model for pressure sensor data
- Video processing with OpenCV
- Signal Capturing from human poses
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2021 Cardiac Medical Data Augmentation
- Train ACGAN on real Cardiac Medical Data in order to generate fake data for augmentation.
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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.
- Features
- Depth Estimation
- Image brightness correction & Color correction models
- Object detection models to find humans and other objects in the Image.
- Image Perspective calculation
- Image blending
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2021 OCR
- Optical Character Recognition on German administrative forms.
- Features
- 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.
Interests
- Guitar
- Video Game
- Ping Pong
- Natrue Lover
- Psychology & Philosophy