Open-source portfolio · GERT · EPSILON · The Backtest That Lied · PJM research

I am a student developer working at the intersection of applied mathematics, machine learning, and scientific software. I build systems that make uncertainty, provenance, assumptions, and failure conditions visible—then test whether their conclusions still hold outside a clean demonstration.

My work has moved from training models and optimizing headline accuracy toward a harder question: when should a computational result be trusted? That question now connects my open-source engineering, energy-risk research, and quantitative decision tools.

Open-source engineering

Verified outcome	Snapshot
Merged upstream pull requests	16
Upstream repositories	14
Open-source organizations	13
Deduplicated reach of recipient repositories	246K+ stars

_{Repository stars are ecosystem-scale context, not stars earned by my patches. Repositories are counted once; methodology and dated evidence are available in the portfolio methodology.}

Selected work:

• MAPIE #953 — added AUROC and AUARC APIs for evaluating uncertainty estimates, including validation, tests, documentation, and review-driven iteration.
• Fairlearn #1674 — integrated confidence-interval detection into the public MetricFrame plotting API with edge-case coverage.
• BeeWare Briefcase #2939 — hardened GitHub Actions through least-privilege permissions, pinned action references, credential-exposure reduction, and zizmor scanning.
• StatsForecast #1175 — added conformal-error prediction intervals and supporting tests.
• NeuralForecast #1563 — implemented FreDF, a loss combining time- and frequency-domain error.

Every merged contribution has an evidence-backed engineering writeup covering diagnosis, implementation, validation, review, and lessons learned: explore all merged work →

Featured work

Project	What it investigates
GERT — Grid Extreme Risk Toolkit · Live	A probabilistic decision-support system for grid stress, tail risk, scenario intervention, and explicit data/model provenance. Its real-model path is gated by calibration rather than promoted when the evidence is insufficient.
EPSILON — Quantitative Decision Lab · Live	A research environment for turning market ideas into falsifiable claims while keeping execution assumptions, provenance, negative evidence, and failure conditions visible.
The Backtest That Lied · Live	A forensic ML study in which a near-perfect signal collapsed after leakage repair and failed again under costs and liquidity constraints—leading to a documented decision to terminate the strategy.
PJM Extreme-Winter Forecasting	Ongoing research into how ordinary annual validation can conceal probabilistic forecast failure during rare cold events. The manuscript remains in development.

How I work

• Negative results are results. I preserve failed tests and stop hypotheses that do not survive their decision gates.
• Reliability needs more than accuracy. I examine uncertainty, calibration, subgroup behavior, leakage, distribution shift, and reproducibility.
• Claims should remain inspectable. I separate simulated from live data, document provenance, and link public statements to code, tests, or primary records.
• Review is part of the engineering. Maintainer feedback, scope reduction, edge cases, and regression tests are recorded—not hidden from the final story.

Current direction

I am connecting two lines of work that began separately: applied modeling and reviewed open-source engineering. My next projects revisit real modeling problems with stronger validation, uncertainty quantification, and fairness analysis—using the same standards I learned while contributing to public scientific software.

Contribution dashboard and merged writeups →