Control-plane architecture for AI & agentic systems: governance as admission control, decision admissibility, and audit-grade evidence.
A hands-on lab showing how “improving” a single metric (AUC/accuracy/F1) can worsen real-world outcomes. Includes metric audits, slice checks, cost-sensitive evaluation, threshold tuning, and decision policies you can defend, so dashboards don’t quietly ship bad decisions.
Model risk validation sandbox for market & credit risk (VaR, ES, EL, backtesting)
Defensible risk evidence for deployed machine learning models
Regime-aware quant risk and market stability monitoring framework.
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