AI Strategist & Machine Learning Engineer
PhD in Applied Mathematics · Bayesian & Probabilistic Machine Learning
TeraSystemsAI · Research in Healthcare, Security, and Industrial AI
I design and build reproducible, uncertainty-aware machine learning systems for real-world decision-making, with particular focus on high-stakes domains such as fraud detection, customer retention, healthcare, and industrial engineering.
My work connects peer-reviewed research with production-grade ML systems, emphasizing interpretability, evaluation rigor, and risk-aware modeling.
| Title | Journal | Year | DOI |
|---|---|---|---|
| Stochastic Inventory Optimization with Coherent Risk Measures: A Decision-Theoretic Framework for Probabilistic Forecasting and Constrained Optimization | Journal of Risk and Financial Management | 2026 | https://doi.org/10.3390/jrfm19030173 |
| Bayesian RAG: Uncertainty-Aware Retrieval for Reliable Financial Question Answering | Frontiers in Artificial Intelligence | 2026 | https://doi.org/10.3389/frai.2025.1668172 |
| Hybrid Naïve Bayes Models for Scam Detection | IEEE Access | 2025 | https://doi.org/10.1109/access.2025.3569216 |
| Enhancing Autonomous Systems with Bayesian Neural Networks | Frontiers in Built Environment | 2025 | https://doi.org/10.3389/fbuil.2025.1597255 |
| Application of Bayesian Neural Networks in Healthcare | Machine Learning and Knowledge Extraction | 2024 | https://doi.org/10.3390/make6040127 |
End-to-end machine learning pipeline for customer churn prediction using real-world tabular data, with a focus on reproducibility, leakage control, evaluation discipline, and explainability.
Repository: https://github.com/lebede-ngartera/customer-churn-risk-ml
Decision-intelligence project integrating probabilistic demand forecasting, constrained optimization, and Monte Carlo risk evaluation for supply chain planning under uncertainty.
The project shows how uncertainty-aware forecasts (
Focus areas:
- Forecast-to-decision linkage
- Optimization under operational constraints
- Risk-aware evaluation via Monte Carlo simulation
- Executive-style decision memos communicating cost, service, and risk tradeoffs
Repository: https://github.com/lebede-ngartera/supply-chain-decision-intelligence
Industrial multimodal AI platform for 3D CAD geometry understanding, combining point-cloud learning, graph neural networks, retrieval, anomaly detection, uncertainty-aware prediction, and generative modeling.
The project unifies 3D geometry, text, and engineering metadata into a shared embedding space to support similarity search, anomaly detection, text-to-shape retrieval, and engineering decision support in CAD/CAE workflows.
Focus areas:
- Multimodal representation learning for industrial data
- 3D geometric deep learning with PointNet++, DGCNN, and GNNs
- Retrieval and anomaly detection for engineering workflows
- Uncertainty-aware property prediction and risk-sensitive modeling
- Interactive ML system design for technical users
Repository: https://github.com/lebede-ngartera/GeoFusion-AI
Implementation and evaluation of hybrid Naive Bayes-based models for real-world scam detection, derived from peer-reviewed research.
Focus areas:
- Bayesian generative modeling under data sparsity
- Cost-sensitive evaluation in highly imbalanced settings
- Uncertainty-aware decision thresholds
Derived from: Hybrid Naïve Bayes Models for Scam Detection (IEEE Access, 2025)
Repository: (in progress)
- Research defines what is theoretically sound
- Engineering determines what is deployable
- Evaluation decides what is trustworthy
I focus on making tradeoffs explicit and uncertainty visible rather than optimizing single-point metrics in isolation.
- Fraud and scam detection
- Customer behavior modeling
- Bayesian and probabilistic machine learning
- Risk-aware AI systems
- Industrial AI for engineering workflows
- Interpretable ML in regulated environments