Will evaluate machine learning approaches for causal discovery from observational data,

including PC and NOTEARS, and compare them against non-causal statistical baselines.

Investigating MEP support design by formulating Structural Assembly synthesis as a feasibility-aware problem. Then applied ML and more specifcally Reinforcment Learning - based solution strategies.

Developed a DNN that analyzes NetFlow records to detect network fragmentation and disturbance events. Engineered features such as packet inter-arrival times, flow duration, and IP flags. After validating the model for robust accuracy, formatted detected anomalies into threat-intelligence and incident-response workflows.

Designing and building a production-grade backend in Golang powering a campus machines platform used by college students, along with a streamlined admin web app. The system is instrumented with Prometheus metrics, backed by SQLite and Redis, and ships with Docker-based deployment for reliability and scale.