eajajhossain
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Machine Learning Engineer specializing in deep learning, graph ML, and applied NLP systems. Experienced in building end-to-end machine learning pipelines from data preprocessing to optimized inference, with hands-on work in neural networks, malware detection, and production-oriented experimentation. Solo author of a research-driven hybrid malware detection framework submitted to IEEE TIFS, focused on robust detection under real-world distribution shift.
it has a complete demonstration of performing experiment tracking using ML flow
Built an end-to-end personal finance intelligence system that ingests raw bank data, detects abnormal spending, explains risks, and provides actionable financial recommendations
Python
A personalized AI system that predicts user behavior from self-reported and device data, detects inconsistencies, and uses reinforcement learning to adaptively guide better habits over time
Python
A production-grade customer churn prediction system that monitors data drift and automatically retrains models over time.
Jupyter Notebook
Production-ready Deep Learning system for automatic invoice data extraction using Bidirectional LSTM neural networks. Achieves 100% accuracy on entity recognition (vendor, invoice number, date, tax…
Python 1
