Über Issa
I am a Deep Learning Engineer with 5 years of experience in computer vision related problems.
I worked with big companies and startups, in R&D and production. Throughout my experience, I acquired a strong knowledge in machine learning/deep learning.
In addition, I was responsible of maintaining the code and appling best practices, in order to serve a clean and flexible architectures.
A brief examples of what I did in my past jobs:
- Worked on a variety of vision problems (document understanding, image segmentation, depth estimation, visual inspection).
- Implement advanced pipelines with TensorFlow & PyTorch.
- Design and optimize deep learning networks for limited resources environment.
- Develop data collection and visualization tools (Selenium, Dash).
- Debug and improve complex architectures and propose new solutions (vision-language models, UNet etc...).
- Refactor and maintain complex code using coding best practices.
After all, I am a problem solver and I am ready to help you benefit from the power of deep learning.
Arabisch
Muttersprachlich oder zweisprachig
Französisch
Muttersprachlich oder zweisprachig
Englisch
Verhandlungssicher
Vor Ort möglich
Paris (bis zu 50 km)
Projekt- und Berufserfahrung
- RYTE FranceComputer Vision EngineerMEDIZINJanuar 2024 - Heute (2 Jahre und 5 Monate)Paris, FrankreichI am working on Visual Document Understanding.Tasks:
- Perform SOTA analysis on different visual document understanding techniques (w/ and w/o OCR).
- Develop a Python scrapper to collect data.
- Develop a web app for visualizing and curating the data.
- Finetune and evaluate an end-to-end large language and vision model for information extraction.
Technologies: Python, Dash, PyTorch, Hugging Face, Swin Transformer, BART. - 7 Sensing SoftwareComputer Vision EngineerHIGHTECHFebruar 2022 - Januar 2024 (1 Jahr und 10 Monate)Paris, FranceI worked on 2 different projects: monocular depth estimation and visual inspections.1. Monocular depth estimation from RGB and dToF sensor:
- Design and optimize deep learning models for limited resources environment .
- Port and benchmark the trained models on mobile phone.
- Apply a statistical analysis study to reduce the gap between real and synthetic data.
- Refactor and apply development best practices (code architecture, design patterns).
2. Visual Inspection- Design and benchmark deep learning solutions for anomaly detection, classification and segmentation.
- Research and implement unsupervised method for labelling assistance.
- Pretrain networks using self-supervised learning techniques (MoCo, SimCLR).
In addition, I proposed, recruited and supervised an intern to work on reducing temporal inconsistency in video depth estimation. - Self EmployedDeep Learning InstructorBILDUNG & E-LEARNINGFebruar 2023 - Heute (3 Jahre und 4 Monate)I created my online course, called Intuitive Deep Learning, where I explain the foundations of Deep Learning in an intuitive and connected way.Tasks:
- Create 10 lessons, 2 hands-on, multiple choice questions covering the foundation of Deep Learning, based on books, papers and my own experience.
- Create animations using Python for an intuitive explanation of complex concepts.
- Design the website landing page, and improve the content format with CSS.
- Prepare the marketing content (logo, videos, animations, posts etc...).
Here is the website: https://intuitive-dl.thinkific.com/courses/intuitive-dl
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Ausbildung und Abschlüsse
- Master of Science in EngineeringTelecom ParisTech2019Master of Engineering (MEng), Data Science
- Master of EngineeringLebanese University - Faculty of Engineering III2019Master of Engineering (MEng), Electrical and Electronics Department