Projects

Developed a machine-learning framework combining supervised and unsupervised methods to detect fraudulent Ethereum accounts with >85% accuracy and <5% false positives, deployed as an interactive Streamlit app.

Built a comprehensive real-time analytics platform processing 10M+ events per day using Kafka, Spark Streaming, and ClickHouse for sub-second query performance.