TRI NHAM

Data Science Research

Led the full EDA and modeling pipeline on the METABRIC breast cancer dataset (2,509 patients) with Dr. Pablo Guillén-Rondón at UHD — missing-data analysis, feature separation via Cohen's d & Phi coefficient, correlation heatmaps, and class balance review across 6 clinical targets. Currently in conference review.

Machine Learning Model Development

Engineered 18 clinical features and trained/compared Traditional ML, MLJAR AutoML, and TabNet under stratified 5-fold cross-validation with SHAP explainability. Gradient Boosting reached AUC 0.998 (NPI High Risk) and 0.952 (HER2+), with SHAP identifying lymph node positivity, NPI score, and tumor size as dominant predictors.

Exploratory Data Analysis (EDA)

Run systematic EDA across projects — missing-data audits, statistical feature separation (Cohen's d, Phi coefficient), correlation heatmaps, and class-balance checks — to guide feature engineering and modeling decisions.

Data Analysis

Delivered structured dataset analysis and reporting for client and internal stakeholders — from ETL-backed business insights at Cognixia to daily liquidity rate reporting at Capital One.

Data Pipeline Engineering

Built and maintained ETL pipelines across Databricks, Snowflake, and Spark at Capital One to support multi-source financial data analysis, reducing downstream errors by 25%.

Data Processing & Manipulation

Transformed and cleaned large tabular datasets — from Cognixia's client ETL workflows to feature engineering for the METABRIC research.

Backend & API Engineering

Translated business requirements into REST API-based data ingestion workflows at Capital One, connecting upstream systems to daily reporting pipelines.

Automation & Data Validation Testing

Built automated data validation pipelines and testing checks at Capital One, cutting manual rework by 40% and improving reporting confidence across teams.

End-to-End Project Delivery

Delivered an in-house replacement for a third-party liquidity reporting dependency at Capital One — owned end to end from design to production — saving the company millions in vendor costs.

Production Support & Bug Fixing

Rotated on-call for pipeline production support at Capital One. When pipelines failed, I dug through logs to isolate root cause — ECS timeout/bandwidth issues, schema mismatches, duplicate records — fixed what I could directly and escalated upstream file issues, keeping same-day ingestion on track for downstream teams.

AI/ML Development

Built DP Tuner, a differential privacy evaluation system that automates ε/δ accounting and hyperparameter tuning for synthetic health datasets.

Research Paper Contribution

Co-authoring a paper on ML-based breast cancer prognosis using the METABRIC dataset, currently under conference review.

Software Engineering

Wrote and maintained production code spanning backend data systems at Capital One and a native mobile app (Histudy) — from data models to UI logic to automated tests.

iOS App Development

Built Histudy, a native iOS app that helps Vietnamese-speaking immigrants prepare for the U.S. citizenship civics test with a bilingual, AI-adaptive tutor and a spaced-repetition learning engine (SM-2), including unit tests for the learning engine and content integrity.

Bachelor of Computer Science

University of Houston

3.5

Master of Data Analytics

University of Houston-Downtown

4.0

Race History

6 half marathons and 1 full marathon so far, plus 1 Ironman 70.3. Full training log and every race is on Strava.

ML for Breast Cancer Prognosis — METABRIC

Investigating whether machine learning can predict breast cancer prognosis and risk stratification from the METABRIC clinical and genomic dataset — 2,509 patients across 6 different clinical targets.

Combining engineered clinical features (NPI risk groups, LN flags, log-mutation, ER×HER2 interaction) with modern ML and AutoML approaches can match or exceed standard prognostic scoring, while SHAP explainability keeps the model clinically interpretable rather than a black box.

Led the full EDA pipeline (missing data analysis, Cohen's d and Phi coefficient feature separation, correlation heatmaps, class balance review), engineered 18 clinical features, ran stratified 5-fold CV comparisons across Traditional ML, MLJAR AutoML, and TabNet, and produced the SHAP explainability that identified lymph node positivity, NPI score, and tumor size as dominant predictors — Gradient Boosting reached AUC 0.998 (NPI High Risk) and 0.952 (HER2+). Currently in conference review.

DP Tuner

A research framework for automatically tuning the differential privacy budget (ε) in synthetic data generators — DP-TVAE and DP-CTGAN — designed for healthcare and HIPAA-aligned experiments.

Instead of picking an arbitrary privacy budget, you can search for the smallest ε that still preserves downstream model utility (e.g. AUROC ≥ target) — giving strong, provable privacy guarantees without over- or under-protecting patient data.

Built the framework end to end — the ε/δ tuning search loop, DP-TVAE / DP-CTGAN integration, and the utility-vs-privacy evaluation harness used to validate the results.

EEG Analytics

An interpretable two-stage EEG decoding pipeline that classifies motor execution vs. motor imagery from multi-channel EEG signals, built for Rice Datathon 2026.

A two-stage, limb-specific decoding approach with band-power and PSD feature engineering can classify motor intent more accurately — and more interpretably — than a single end-to-end black-box model.

Built the signal processing and feature engineering pipeline (band power, PSD), the two-stage classification approach, and the neural visualizations used to interpret the results.

Stockd

An AI-powered stock thesis tracker — know when the story behind a stock changes before the price does.

Price alone doesn't tell you when your investment thesis has broken. Tracking the thesis itself — news, fundamentals, narrative shifts — surfaces the moment the story changes, ahead of the price catching up.

Built it end to end — product concept, backend, and the deployed web app.