A systems-thinking essay that explains why failure rarely happens suddenly. It shows how slow drift, accumulating pressure, and weakening buffers push systems toward collapse long before outcomes change, and why prediction-focused analytics miss the most important phase of failure.

AML Checker detection and analysis tools for cryptocurrency transactions using machine learning and blockchain data. Ensuring compliance and reducing money laundering risks.

A systems-thinking essay that reframes failure as a gradual transition rather than a discrete outcome. It explains how pressure accumulation, weakening buffers, and hidden instability precede visible collapse, and why prediction-based models arrive too late to prevent failure in human-centered systems.

An interpretable battery health engine that detects hidden points of no return instead of just predicting health %. It models stress, buffer, and degradation intensity, discovers Stable/Drifting/Irreversible regimes via GMM, and learns simple Decision Tree thresholds, with a Streamlit app for diagnostics and what-if scenarios.

A machine learning classification approach for risk detection in battery cell manufacturing

CONTEXTIQ : AN ADVANCED FRAMEWORK FOR CONTEXTUAL ANALYSIS AND RISK DETECTION

AI-powered contract analysis tool that extracts clauses, highlights risky terms, summarizes documents, and answers questions using NLP. Built with Streamlit + Python.

Network Sentinel is a modular, protocol-aware port scanner designed for architects, SOC teams, and security engineers. It scans TCP ports across defined targets, flags known vulnerabilities via CVE-style mapping, and visualizes results through a Flask dashboard.

AI-powered construction project monitoring system for Procore - intelligently analyzes daily logs and generates proactive alerts to prevent cost overruns and schedule delays

🔄 Transform failure into an opportunity for growth in analytics and system design by focusing on transitions rather than outcomes.

A complete Android malware analysis system that pulls APKs from a connected device, performs static analysis, generates ML features, predicts malware risk using a trained RandomForest model, and displays everything through an interactive Streamlit dashboard with detailed per-app security insights.