Hello, I'm Saif Mohammed ML/DL Engineering & Research

Passionate about AI, Deep Learning, Healthcare, and solving real-world problems with data-driven approaches. Interested in Computer Vision, NLP, and their intersection with medicine.

Saif Mohammed

About Me

Saif Mohammed

I am an engineering student focused on machine learning systems that solve practical problems, especially where AI meets healthcare. I care about building models that are accurate, useful, and understandable in real-world settings.

Skills

  • Core: Python, Deep Learning, Model Optimization, Experiment Design
  • Tooling: PyTorch, TensorFlow, NumPy, Pandas, Scikit-Learn, Docker, Linux
  • Focus Areas: Computer Vision, NLP, LLM systems, AI for healthcare

Currently Exploring

  • Efficient training and deployment strategies for deep learning models.
  • Medical applications of computer vision and predictive modeling.
  • Open-source contributions in modern ML tooling.

Experience

A quick timeline of roles and milestones that shaped my ML journey.

Jul 2025 - Aug 2025

Undergraduate Research Assistant, Cairo University

  • Developing a longitudinal Multiple Sclerosis lesion tracking and labeling system for multi-timepoint MRI data.
  • Implementing medical image processing and registration pipelines using NumPy, SciPy, ANTsPy, and NiBabel.
  • Exploring state-of-the-art deep learning models for lesion segmentation and tracking, and evaluating performance on clinical datasets.

Jul 2025 - Aug 2025

AI Developer Intern - Siemens EDA

  • Designed and implemented LLM-powered features for internal engineering tools using LLM APIs and Retrieval-Augmented Generation (RAG).
  • Built structured prompt pipelines to improve response relevance and reliability.

Featured Projects

Here are some of my recent projects that showcase my skills and passion for AI

Seq2Seq Model From Scratch

Paper Implementation | Deep Learning, Sequence Models, LSTM

Implemented a Seq2Seq architecture based on Sutskever et al.'s paper "Sequence to Sequence Learning with Neural Networks".

  • Developed an English–French translation system using this architecture with robust data preprocessing.
  • Trained on Eng–Fra parallel 34,802 pairs corpus achieving 38.32 BLEU score.
  • Achieved 172.75 sentences/sec inference speed.
Deep Learning Seq2Seq LSTM NLP Research

Alzheimer's Disease Modeling with PINNs

Collaborative Research Project | Machine Learning, Partial Differential Equations

Developed Physics-Informed Neural Networks (PINNs) to model Alzheimer's disease progression:

  • Enhanced model accuracy by optimizing a PINN architecture to capture tau protein diffusion-reaction dynamics, resulting in more reliable predictions of unknown PDE reaction terms.
  • Expanded code functionality by incorporating machine learning techniques to predict reaction terms, leading to a robust and adaptable codebase.
  • Advanced domain insights by integrating symbolic regression for interpretable reaction terms, thereby deepening understanding of Alzheimer's pathology.
Python Neural Networks PDEs Research

Cars Model Classification

GitHub | Deep Learning, Fine-tuning, MobileNetV2

Fine-tuned MobileNetV2 on the Stanford Cars dataset (16k+ images, 196 classes).

  • Achieved 86% test accuracy on large-scale car model classification.
  • Explored transfer learning and fine-tuning strategies for improved performance.
Deep Learning Fine-tuning MobileNetV2 Stanford Cars

Latest Posts

Thoughts on AI, Machine Learning, Deep Learning, and my engineering journey

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Get In Touch

I'm always interested in new opportunities and collaborations. Let's connect!

Let's Connect

Whether you have a project idea, want to collaborate, or just want to say hello, I'd love to hear from you!

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