DATA SYSTEMS / APPLIED ML / TRUSTWORTHY AI
Data Science · Machine Learning · Data Engineering
I build reliable data and ML systems, with a focus on distribution shift, rigorous evaluation, and secure AI agents.
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🛡️ TaintGateA runtime guard that evaluates protected AI agent tool calls before execution. Method: Tracks input provenance, evaluates security findings, and applies deterministic policy to return Validation: A local regression suite checks expected decisions across 12 included attack scenarios. The repository also includes framework and MCP integrations. |
A benchmark investigating early warnings of synthetic-data-induced model degradation. Design: 3 datasets × 3 mechanisms × 5 seeds × 11 levels = 495 controlled conditions. Evaluation: Compares drift signals using discrimination and warning lead time, and examines whether thresholds transfer across datasets. |
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A modular customer ML platform covering churn prediction, lifetime value, and segmentation. System: Feature engineering and model evaluation connect to SHAP explanations, MLflow experiment tracking, and FastAPI serving, with automated tests and CI. |
A shipment data platform for operational risk analysis. Architecture: Databricks Bronze, Silver, and Gold layers prepare shipment data for delay prediction, vendor risk analysis, and SQL dashboards. |
| Project | Technical focus |
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| HealthLynked Provider Pipeline | Record changes · Duplicate detection · Confidence scoring · Human review |
| Fraud Detection & Risk Modeling | Imbalanced classification · Logistic regression · Random forests · Precision–recall |
| Customer Churn Analytics | Customer behavior · Churn prediction · Power BI reporting |
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controlled conditions DriftForge ↗ |
local attack checks TaintGate ↗ |
transactions in dataset Fraud Detection ↗ |
| DATA & COMPUTE | MODELING & EVALUATION | SYSTEMS & ANALYTICS |
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