About
My Introduction
M.Sc. in Biomedical Engineering, active in the field of AI. Interested in Machine Learning and Deep Learning. Awarded elite points by the National Elites Foundation for contributing to the development and teaching of the "AI and Machine Learning in Medical Data" course for graduate students at the university.
Experience
My journey in the academic & industryMaster's degree - Biomedical Engineering
Shahed University, TehranBachelor's degree - Biomedical Engineering
Shahed University, TehranTeacher Assistant for Artificial Neural Networks
Shahed UniversityTeacher Assistant for Machine Learning and Deep Learning in Medicine
Shahed UniversityComputer Lab Teacher
Shahed UniversityStudent work in the Computer Center
Shahed UniversitySMT Specialist (Surface Mount Technology)
Pooyandegan Rah SaadatMedical Ventilator Compressor Specialist (Respina Air)
Pooyandegan Rah SaadatBiomedical Engineer Internship
Pooyandegan Rah SaadatTeacher Assistant of Numerical Calculations
Shahed UniversitySkills
My technical & other skillsArtificial Intelligence
juniorPython
70%Machine Learning
Scikit Learn
70%Pandas
70%Numpy
55%Matplotlib
75%Deep Learning
Pytorch
75%Tensorflow-Keras
40%Computer Vision
70%Signal Processing
70%Natural Language Processing
55%Computer Engineering
GitHub
70%Docker
50%SQL
40%App Prototyping
55%Front end
30%ICDL
80%Biomedical Engineering
Medical Equipment Maintenance & Repair
75%SMT Equipment Programming
85%Medical Device Testing & Calibration
80%Additional Skills
Research & Information Retrieval
75%Teaching & Mentoring
70%Eager to Learn & Continuously Improve
95%Teamwork & Collaboration
85%Research
Motor Imagery EEG Signals: Multi-Task Classification and Subject Identification with a Lightweight CNN
SSRN · Feb 26, 2025Author(s): Amir Hossein Fouladi, Prof. Mohammad Pooyan
Projects
Biomedical Data Processing
Gastrointestinal Medical Image Segmentation
This project aims to develop a deep learning model for segmenting the stomach and intestines in MRI scans of cancer patients, improving radiation targeting and reducing manual intervention.
In Dev
AI in Biomedical Data
This educational repository focuses on working with three types of medical data: tabular data, ECG and EEG signals. It provides implementations of machine learning and deep learning models for processing and analyzing these medical data, with practical projects based on recent research articles. Additionally, you can refer to the table below to access related instructional videos on YouTube.
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Pneumonia Classifier Using Chest XRay
In this notebook, we perform a binary classification on chest X-ray images to determine whether a person has healthy lungs or is diagnosed with pneumonia. For this classification, we used a custom deep convolutional neural network (CNN) model and achieved an accuracy of 95% on the test set.
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EEGNet
This code implements the EEG Net deep learning model using PyTorch. The EEG Net model is based on the research paper titled "EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer Interfaces". EEG Net is a compact convolutional network that is both lightweight with few parameters and powerful in processing raw EEG signals.
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Stable Diffusion GUI App
A Flask GUI application that lets users input text prompts, generates images using Stable Diffusion via a Hugging Face API, and displays the result with a download option.
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Yolo v8 App
A user-friendly interface built with Streamlit that performs real-time computer vision tasks including object detection, segmentation, video tracking, and pose estimation.
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EyeTracker and HandGesture with CVZone
Two well-known computer vision tasks, eye pupil tracking and hand gesture recognition, are addressed in this repository using the OpenCV and CVZone libraries in a Python environment.
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Age, Gender, and Race Estimation with ResNet
Estimate age, gender, and ethnicity from facial images using a ResNet model. This project utilizes the UTK Faces dataset, by cropping the facial region, to train two ResNet models for facial attribute estimation.
In Dev
Object Detection Web App
This is an object detection web app where users can upload an image, select a detection model, and view the processed image with detected objects. The result is downloadable for further use.
View github code
Speech Commands Classification
In this notebook, we aim to recognize speech commands using classification. For this purpose, we used the SPEECHCOMMANDS dataset and the deep convolutional model M5. The code is written in Python and designed for the PyTorch platform.
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