Decision Support Systems · AI & Data Science

AI that makes people better decision-makers — not AI that decides for them.

I build systems that sit next to human judgment, not in front of it: transparent models, honest uncertainty, and interfaces that explain themselves. This site collects that work — from a workforce income analytics platform built on 48,790 U.S. Census records to decision-support systems for healthcare, credit risk, and customer retention.

On Human–AI Relationship

Every project on this site is built on the same working belief: the goal of applied AI is not to replace a person's judgment but to make that judgment sharper, faster, and better informed. A model that quietly makes the call is a liability. A model that shows its reasoning, flags what it doesn't know, and hands a clear decision back to a human — that's a tool worth trusting.

01

Augment, don't replace

Systems are designed as decision support — surfacing patterns, risks, and probabilities for a human to weigh, rather than issuing verdicts a person is expected to accept on faith.

02

Transparency over accuracy theater

A model's headline metric is never the whole story. Every project here documents what the data can't tell you — missingness, top-coding, sampling bias — alongside what the model gets right.

03

Built for the person who has to act

A dashboard nobody reads and a model nobody trusts are the same failure. Every system is designed around the actual decision-maker: what they need to see, in language they already use.

The Pento‑Helix

Underneath those three principles sits a broader frame I work from: the Pento‑Helix. Pento, for five — a structure meant to stay portable, flexible, and adaptable across contexts, not fixed to one project. Helix, because it behaves like DNA: one constant structure that expresses itself differently everywhere it's deployed, the way RNA carries out what DNA encodes.

01Human–AI collaboration and synchronization
02Human–AI harmony
03Each side strengthening the other's skills and expertise, while understanding the other's limitations — mutual respect
04Shared social responsibility for what gets built
05Progressive, together — the constant thread across every project, and what this site's mark is built around: a dot held steady at the center — the Tao — with a spiral of motion, a Milky Way, turning around it.

The first four pillars above are how the Pento‑Helix takes shape in the Human–AI Augment Systems work collected on this site. Other projects will express their own version of the same four; the fifth never changes.

Five decision-support systems, one working principle

One project is built out end-to-end as a full case study — data pipeline, BI layer, and tuned ML models. The other four all have full write-ups below too — real data, transparent methods, and an honest account of what each model can and can't tell you.

Healthcare

Nurse Augmenting System for Biomedical Intelligence and Personalized Care Augmentation

Eight independently trained models — spanning linear, tree, kernel, boosting, and ensemble methods — converged on the same five predictors of heart-failure readmission. A hand-engineered clinical score, not raw accuracy, was the point.

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Finance

AI-Powered Loan Default Risk Decision Support System

A 12-model benchmark on 29.9 million LendingClub records — where an obvious leak was caught and removed, and a suspiciously perfect score that remained was investigated and explained rather than just reported.

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Marketing

AI-Powered Marketing Decision Support System for Customer Retention

Nine models were benchmarked on IBM's Telco Churn dataset — and the highest-accuracy one wasn't picked. The production model was chosen for matching a real business threshold instead.

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Healthcare

Medication Adherence & Healthcare Claims Analysis

A star-schema claims model on 24,084 diabetes and hypertension patients — including an honest report of the one model, out of five, that quietly failed.

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