AI Research Strategist | Applied Mathematician | Machine Learning Engineer
Founder & Chief Research Architect, TeraSystemsAI LLC
Ph.D. in Applied Mathematics • Trustworthy AI • Bayesian Machine Learning • Environmental Intelligence • Decision Intelligence
I design and engineer trustworthy AI systems that support high-stakes decision-making through scientific rigor, uncertainty-aware learning, and evidence-grounded reasoning.
My work bridges peer-reviewed research and production-oriented AI engineering, translating advances in applied mathematics, probabilistic machine learning, and artificial intelligence into systems that remain transparent, auditable, and reliable under real-world conditions.
My current research focuses on:
- Trustworthy AI
- Uncertainty Quantification
- Evidence-Governed AI
- Environmental Intelligence
- Healthcare AI
- Fraud Detection
- Decision Intelligence
- AI Governance
Rather than asking:
"Can an AI system produce an answer?"
my work explores a more fundamental question:
"Can the available evidence justify that answer, and how confidently should it be trusted?"
| Title | Journal | Year | DOI |
|---|---|---|---|
| BRAG: Bayesian Retrieval-Augmented Generation; A Methodological Framework for Evidence-Governed Decision Support | Machine Learning and Knowledge Extraction | 2026 | https://doi.org/10.3390/make8060151 |
| 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 |
Complete publication list: https://www.terasystems.ai/publications.html
- Trustworthy AI
- Bayesian & Probabilistic Machine Learning
- Uncertainty Quantification
- Environmental Intelligence
- Evidence-Governed Retrieval-Augmented Generation
- AI Governance
- Decision Intelligence
- Healthcare AI
- Financial AI
- Industrial AI
- Explainable AI
- Reproducible Machine Learning
A scientific initiative advancing evidence-bounded environmental interpretation through the Environmental Concordance Framework (ECF), quantified reliability, deterministic reasoning, and transparent environmental intelligence.
Explore
- https://www.terasystems.ai/environmental-intelligence.html
- https://www.terasystems.ai/indexTEI.html
- https://www.terasystems.ai/tei-collaborative-network.html
End-to-end machine learning pipeline emphasizing reproducibility, leakage prevention, explainability, calibration, and disciplined evaluation.
Repository
https://github.com/lebede-ngartera/customer-churn-risk-ml
Decision intelligence integrating probabilistic forecasting, constrained optimization, coherent risk measures, and Monte Carlo simulation for operational planning under uncertainty.
Repository
https://github.com/lebede-ngartera/supply-chain-decision-intelligence
Industrial multimodal AI platform integrating CAD geometry, engineering metadata, graph neural networks, retrieval, anomaly detection, and uncertainty-aware prediction.
Repository
https://lebede-ngartera.github.io/Industrial-3d-geometry-ai/
Implementation of peer-reviewed probabilistic models for scam detection in highly imbalanced environments.
Repository
In Progress
I believe trustworthy AI emerges from the integration of:
- Scientific Research
- Engineering Discipline
- Transparent Evaluation
Research establishes scientific foundations.
Engineering transforms theory into deployable systems.
Evaluation determines whether a system deserves trust.
My work emphasizes making uncertainty explicit, quantifying reliability, and supporting accountable decision-making rather than optimizing single-point performance metrics.
- Trustworthy AI
- Environmental Intelligence
- Bayesian Machine Learning
- AI Governance
- Evidence-Governed AI
- Decision Intelligence
- Healthcare AI
- Financial AI
- Industrial AI
- Document Intelligence
- Uncertainty Quantification
- Explainable AI
Website • Publications • LinkedIn • GitHub
"Building AI systems that know not only what they can infer, but also when the available evidence is insufficient."