AI Engineer · ML Engineer · Healthcare AI
End-to-end machine learning pipelines on real-world clinical data — from FDA adverse event prediction to mental health NLP, each deployed as a live interactive application.
End-to-end ML on 1,000 real ClinicalTrials.gov records. Predicts whether a trial will produce publishable results. 19 features engineered from 138 raw fields, SHAP explainability identifies enrollment size and trial phase as top predictors.
Biomedical NLP RAG pipeline on 967 PubMed abstracts (2023–2026). FAISS vector store with all-MiniLM-L6-v2 (384-dim) semantic search. Extractive summarization achieving ~92% compression with real PubMed citation links.
ML pipeline on 467 real FDA FAERS adverse event reports. XGBoost with SMOTE oversampling for class imbalance. Real-time physician-facing risk scoring — input drug name + patient demographics, get predicted adverse events and risk level.
XGBoost model predicting 30-day hospital readmission risk for diabetic patients using 20 engineered clinical features — HbA1c, glucose, BMI, prior admissions, discharge disposition. Per-patient SHAP risk decomposition in dashboard.
7-class NLP classifier on 103,000 social media posts: anxiety, bipolar, depression, normal, personality disorder, stress, suicidal. TF-IDF + LR baseline at 88.1% accuracy. DistilBERT fine-tuning script included. Privacy-first with crisis resource integration.
► Open to Opportunities
Seeking AI Engineer, ML Engineer, or Healthcare AI roles.
Available for full-time positions and collaborations.