Yasien Ghalwash
Machine Learning Engineer
Biography
I’m Yasien, a recent graduate from the Systems and Biomedical Engineering Department at Cairo University, Egypt. Currently, I am working as an AI Engineer at Inspire for Solutions Developments since August 2023.
My areas of interest include Artificial Intelligence, Computer Vision, Image Processing, and Sport Engineering. My GPA is 3.5, and I have an IELTS score of 7.0.
Interests
- Machine Learning
- Computer vision
- Biomedical Engineering
- Sport Engineering
Education
BSc in Biomedical Engineering, 2023
Cairo University
Experience
AI Engineer
- Utilized IBM Watson Assistant focused on using AI to power virtual assistants. Use and create artificial intelligence software including machine learning and natural language processing to help people and organizations solve problems more effectively.
Research and Development Engineer
- Contributed to Building End to end System for Automatic Segmentation and Classification of Breast Cancer Ultra-Sound Images as part of my graduation project.
- Used powerful Deep Neural Networks like Unet, attention Unet, and Efficient Net.
- Optimized AI models for both CPU and GPU platforms to enhance performance and maximize computational resources.
- Implemented Docker containers to encapsulate the various components of the system, enabling easy deployment and management.
- Utilized Azure Virtual Machines to host and run the containerized system.
Biomedical Engineer Intern
- Know the main components and Basics of MRI, CT, PET/CT and medical laboratory equipment
Projects
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Developed an end-to-end PACS-integrated system for the automatic segmentation and classification of breast cancer ultrasound images using state-of-the-art deep-learning techniques
Versatile image processing methodology to auto-detect coverage bounding boxes on brain MRI images based on stated anatomic landmarks
Facial detection model that learns the latent variables underlying face image datasets and uses this to adaptively re-sample the training data.
Using U-Net architecture to effectively segment the stomach and intestines in MRI scans in order to improve the cancer treatment to avoid high doses of radiation to healthy tissues.
Implementing U-Net CNN by Tensorflow. Building U-Net Encoder and Decoder and using skip connections between them to segment input images into different classes.
Training LSTM Model implemented by TensorFlow on music notes after processing them, and eventually the model was able to create new notes.
Curve fitting and interpolation are among the most useful tool in signal processing and data science! this desktop app opens the door to engineers to easily visualize how the process is done using one chunk or multiple chunks, Also the error map shows us how our model is far from the original data and what is the percentage error.
A desktop application created by PyQT. Can be used as a Music Player. In addition, can do signal processing on songs to supress certain musical instruments or emphasize certain instruments. There is some built-in virtual musical instruments which you can play with and generate notes.
Image Processing GUI using Python.Implemented some image processing algorithms applicable on both RGB and grayscale Images with the ability to modify the kernel size (filter strength). Filters implemented and applied Histogram Equalisation, Mean Filter, Median Filter, Low Pass Filter, High Pass Filter, Gaussian Filter, Sobel Filter, and Laplacbian Filter.
This is a web-based project. created with ReactJS. It can Load DICOM images and navigate through them. There is some widgets to add more functionalities to the project.
Contact
Feel free to reach out to me at any time. My current timezone is UTC+2.