Machine learning engineer working across computer vision, NLP and applied ML — from reconstructing damaged Akkadian tablets with an explainable ByT5 ensemble (2nd place, Innoverse Expo AI Programming Challenge) to Show-Attend-and-Tell captioning in PyTorch and retrieval-augmented question answering.
I care about work that is reproducible and honestly measured: every project below reports a baseline, states what its numbers do and do not mean, and runs top to bottom from a clean clone.
INNOVERSE — AI Archaeologist · 2nd place, Innoverse Expo AI Programming Challenge Explainable AI for reconstructing incomplete Akkadian texts. A ByT5 ensemble backed by retrieval from attested tablet lines, knowledge graphs and calibrated confidence — every reconstruction is labelled as an attested match, a cited parallel or a model estimate, so an inference is never presented as historical fact.
Image Captioning with Visual Attention PyTorch implementation of Show, Attend and Tell — ResNet-50 encoder, Bahdanau attention, LSTM decoder with beam search and BLEU evaluation.
Traffic Accident Risk Prediction End-to-end classification pipeline with threshold tuning and seasonal models. ROC-AUC 0.952, F1-macro 0.735.
Medical RAG Assistant Retrieval-augmented QA over a medical corpus — FAISS + sentence transformers + LangChain, with source citation and a Streamlit interface.
OCR Translator Tesseract OCR with a measurable preprocessing pipeline (projection-profile deskew, four tuning profiles), translation caching and SQLite history.
Tehran House Price Prediction XGBoost over 3,456 listings. R² 0.768 — log-transforming the skewed target beat the raw model by 4 points.
Digital Health Analyzer Screen-time and lifestyle classification with a rule-based recommendation layer and weekly trend reporting.
Also: Computer Vision Lab · Recommender Systems · TTS Studio · ML Notebooks
Python · PyTorch · Transformers · scikit-learn · XGBoost · OpenCV · LangChain · FAISS · pandas · Streamlit
- Email: mr.danialgithub@gmail.com
- GitHub: @MrDanial-Rafiee