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ayesha_shahid17

AYESHA SHAHID

@ayesha_shahid17

First author ML researcher, RAG systems and academic editing

Pakistan
Inglese, Urdu
Alcune informazioni sono riportate in lingua inglese.
Chi sono
Published ML researcher (first author, Springer Nature, 2026) with a model validated at 96% accuracy and 100% sensitivity. I build production ML/AI systems, RAG applications, prediction APIs, and deployed models, and I edit and strengthen technical research papers for people who've already drafted their own work. Check my gigs below for either service.... Continua a leggere

Competenze

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ayesha_shahid17
AYESHA SHAHID
offline • 
Tempo di risposta medio: 1 ora

Consulta i miei servizi

Integrazioni IA
I will build and deploy a production rag chatbot for your pdfs and documents
A partire da90 USD / progettoMaggiori dettagli
editing copy
I will technically edit and proofread your ml or ai research paper
A partire da15 USD / progettoMaggiori dettagli

Portfolio

Esperienza lavorativa

Self_Employed

Machine Learning Researcher

Self Employed • Lavoratore autonomo

Dec 2023 - Present • 2 yrs 10 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.