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.

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.

An explanation-first HR analytics system that reconstructs why employee exit becomes rational. Instead of predicting attrition, it generates human-readable exit narratives by decomposing pressure and retention forces, adding peer context and counterfactual interventions to reveal how stability erodes over time.

An early-warning system that models disasters as instability transitions rather than isolated events. It combines force-based instability modeling with an interpretable ML escalation-risk layer to detect when hazards become disasters due to exposure growth, response delays, and buffer collapse.

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.

A systems-thinking essay arguing that most optimization quietly trades away buffers, slack, and resilience to make present metrics look better. It reframes efficiency as borrowing stability from the future, and shows how education, workforce, infrastructure, markets, and hardware all get optimized into fragility.

A long-form systems essay arguing that machine learning fails when used as an automated decision-maker in unstable environments. It reframes ML as an early-warning instrument that exposes pressure, instability, and shrinking intervention windows, preserving human judgment instead of replacing it with late, brittle decisions.

An interpretable early-warning engine that detects academic instability before grades collapse. Instead of predicting performance, it models pressure accumulation, buffer strength, and transition risk using attendance, engagement, and study load to explain fragility and identify high-leverage interventions.