Breaking Barriers Hackathon
Real-time anomaly detection system deployed on AWS. Top 8 of 32 teams at AWS × Deloitte × AT&T.
Result: Finalist — Top 8 of 32 Teams · Organizers: AWS × Deloitte × AT&T · Stack: Python, XGBoost, AWS Lambda, S3, Amazon Location Service · Year: 2025
Overview
Built a real-time crowd anomaly detection system for simulated tail-risk events (sudden crowd movements, stampede precursors) within a 24-hour hackathon. The system ingests LLM-generated synthetic event streams and surfaces anomalies on an interactive live map.
Data Pipeline
- Implemented a 0.5–1s micro-batching pipeline to efficiently process high-velocity LLM-generated synthetic data streams in near real-time.
- Engineered lagged spatio-temporal features via client-side sliding windows, enabling stateless backend processing with no session state required.
Model
- Trained an XGBoost classifier for binary anomaly detection on a highly imbalanced dataset.
- Tuned classification thresholds via PR AUC (Precision-Recall AUC) to aggressively minimize False Negatives, prioritizing recall for tail-risk events where missed detections carry high cost.
Deployment
- Deployed ultra-low latency model inference via AWS Lambda with end-to-end response time <400ms.
- Streamed anomaly triggers to an S3-hosted dashboard using Amazon Location Service and MapLibre for real-time geospatial visualization.
- Architecture is fully serverless and auto-scales with event volume.