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AYESHA SHAHID
@ayesha_shahid17
ML Researcher, Healthcare AI, RAG and LLM Apps, Springer Published
Pakistan
Englisch, Urdu
Über mich
Published ML researcher (Springer Nature, 2026) specializing in healthcare AI, RAG applications, and production ML systems.
I've trained 15+ model architectures, including BiLSTM, CNN, XGBoost, and LLMs across medical and NLP domains. I build RAG apps with LangChain and FAISS, ML pipelines with FastAPI and Docker, and deep learning models for classification and prediction.
Every delivery is clean, documented, and production-ready, not just a notebook.
Kompetenzen
AYESHA SHAHID
offline •
Durchschnittliche Antwortzeit: 1 Stunde
Meine Dienstleistungen
KI-Integrationen
I will build and deploy a production rag system
Arbeitserfahrung
Machine Learning Researcher
Self Employed • Selbstständig
Dec 2023 - Present • 2 yrs 6 mos
Conducted supervised machine learning and deep learning research for clinical prediction and decision support systems across multiple medical domains, including diabetes, brain tumor detection, ASD classification, and cardiovascular disease risk. Published first-author research in the International Journal of Diabetes in Developing Countries (Springer Nature, 2026). Developed a stacked BiLSTM Decision Support System achieving 96% accuracy and 100% sensitivity on an independent clinical test set, outperforming all traditional ML and deep learning baselines. Built and deployed production ML systems, including a Diabetes Risk Prediction API (XGBoost + Random Forest ensemble, FastAPI, Docker) and a Medical RAG Assistant (LangChain, FAISS, Llama-3.3-70B, Streamlit), both live on HuggingFace Spaces. Trained and evaluated 15+ model architectures, including BiLSTM, LSTM, CNN-LSTM Hybrid, XGBoost, Random Forest, SVM, KNN, VGG16, ResNet50, and Explainable Boosting Machines across medical imaging, NLP, and tabular domains.
