SMOKE: NBA Shot-Making Metric — Applied Machine Learning & Sports Analytics
PythonGradient-Boosted TreesEmpirical BayesStatistical Validation
A transparent, validated measure of NBA shot-making: actual field goal percentage minus what a shot-difficulty model expected, with confidence intervals on every value.
- Led a four-person ASU capstone team building a shot-difficulty model over NBA tracking data, then continued the research as SMOKE (Shots Made Over Known Expectation), released as a fully reproducible public repository. Abstract submitted to the 2027 MIT Sloan Sports Analytics Conference with two teammates as co-authors.
- Modeled 128,069 tracked shots from 14 release-time features with gradient-boosted trees cross-fitted by game (held-out AUC 0.64 vs. 0.50 base rate); shrunk player estimates with DerSimonian-Laird empirical Bayes and published every value with bootstrap 95% intervals.
- Validated with eight tests, each with its success criterion written down before it ran: at least as stable year over year as effective FG% (0.48 vs. 0.43), adds information about next-season shot-making beyond past efficiency (adjusted R² 0.09 to 0.16, p < 0.001), and penalizes no playing style; only 53 of 266 qualified players separate from league average in a season.
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Multi-Architecture Generative Content Studio — Deep Learning Capstone
PyTorchTransformersDiffusionGANsCNNsRNNs
A unified generative system that produces story text, scene illustrations, character portraits, and background music from a single prompt.
- Built a unified generative system producing story text, scene illustrations, character portraits, and background music from a single prompt.
- Coordinated five architectures in one pipeline: GPT-2 for the story, Stable Diffusion 1.5 with LoRA and ControlNet for scenes, StyleGAN2 with GAN inversion for consistent characters, VGG style transfer for a unified look, and an LSTM trained on 10k+ MIDI files for music.
- Benchmarked pre-trained diffusion against an untrained DCGAN baseline for the build-versus-reuse decision, and documented why that comparison says nothing about GANs as an architecture, including the FID noise floor that makes small score gaps meaningless.
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Tempo — AI Scheduling Assistant
FastAPIFlutterPostgreSQLLLM / NLPConstraint Optimization
A cross-platform AI scheduling app that generates and compares alternative day and week plans.
- Built a cross-platform scheduling app: a FastAPI backend with a constraint-based engine that orders tasks, builds the daily timeline, and resolves conflicts, plus a Flutter client.
- Added an "Ask Tempo" assistant that calls the Claude API server-side with tool use, so a plain-English request can read and change the user's schedule.
- Backed by SQLAlchemy + Alembic, PostgreSQL, and Firebase auth; 380+ backend tests at about 84% coverage behind an enforced 80% coverage gate.
PSID Panel Dataset Analysis — Longitudinal Economics Project
PythonPandasStatsModelsPanel Data
A longitudinal analysis of PSID microdata on U.S. income and wealth dynamics across five survey waves.
- Reconciled Panel Study of Income Dynamics microdata across 13 survey waves, mapping 117 wave-specific variable codes to 9 stable variables, and built an 18,740-row analysis panel covering five waves (1999 to 2015).
- Built the full pipeline: cleaning, reconciliation, regression modeling, and visualization, with Gini and top-decile inequality measures implemented from scratch.
- Found wealth roughly twice as unequal as income (wealth Gini ~0.85 vs income Gini ~0.45), with the median wealth-to-income ratio rising from 1.25 to 2.05 across the panel.
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Scientific Visualization Pipeline (ParaView) — Self-Directed
ParaViewparaview.simpleVTKNumPy
A fully Python-scripted ParaView pipeline for 3D scientific data visualization.
- Built a NumPy/VTK dataset (3D Gaussian concentration field + analytic ABC-flow velocity) and a fully Python-scripted ParaView pipeline (paraview.simple).
- Generated volume renderings, Contour isosurfaces, slices, and Stream Tracer streamline tubes, with automated figure and orbit-animation export.
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