nooran ishtiaq

I’m a Data Science student and intern who builds machine learning and NLP systems with an emphasis on modular, reusable pipelines and clear documentation of experimental results. In research at Deepfake Detection, I developed and evaluated deepfake classification models using machine learning and computer vision, working through large video datasets while presenting findings to research teams. In my Data Science & NLP internship, I built end-to-end NLP pipelines for preprocessing, tokenization, and feature extraction, implementing ETL workflows with Python, SQL, and cloud environments.

I also turn these skills into practical products and deployments—like an AI-powered telehealth platform using a 3-tier architecture with real-time consultations and secure backend APIs, and a multi-agent AI system (LaunchMind) built with CrewAI to generate product and marketing outputs through LLM-driven workflows. From there, I’ve applied MLOps thinking to end-to-end fraud detection pipelines with MLflow tracking, CI/CD, monitoring (Prometheus/Grafana), and drift-based retraining, and I’ve built recommendation and decision-making systems using ETL pipelines and trained models for real-time behavior.