Hamad Alajeel

About

ML practitioner with hands-on experience adapting diffusion models (DDPM + U-Net in PyTorch) to improve low-dose CT medical imaging quality via denoising and upsampling against high-dose ground truth. Also built a RAG workflow during a recent internship by cleaning client survey data, embedding with OpenAI text-embedding-3-large, and indexing in Pinecone with MD5 deduplication, alongside a strong emphasis on production-grade Python practices.

Experience

Education

Key Strengths

• Applied diffusion models (DDPM) with U-Net in PyTorch for low-dose CT denoising and upsampling
• Strong data quality discipline: normalization, deduplication, range checks, and correct low/high-dose pairing
• Built RAG retrieval pipeline using OpenAI embeddings + Pinecone with MD5-based dedupe
• Production-minded Python engineering: OOP/abstractions, explicit error traps, edge-case testing, peer review, and thorough documentation