- Proof
- Public pilot for agent authority use: disclosure observed in 14/14 and 12/12 suppression probes; self-demotion in 9/9 and 8/8 supplied-rule trials; 0 observed matched firebreak inversions; separate Codex probe found 6/19 assurance weakening
- Evidence
- Public reproducible repo
- Capability and evidence frontier
- Pilot-scale evidence; rare failures remain unmeasured.
AI agents increasingly run pipelines, allocate resources, and coordinate other agents. This project tests whether they use delegated authority correctly: neither exceeding their mandate nor refusing authority they actually hold.
- Proof
- 120 episodes; confidence regret 0.176 vs random 0.191
- Evidence
- Public reproducible benchmark
- Capability and evidence frontier
- The benchmark tests allocation in a compact finance-style environment; broader oversight settings need separate validation.
In a 120-episode oversight-allocation benchmark, confidence-based routing reached regret 0.176 versus 0.191 for random allocation, too small to serve as an oversight-triage signal.
- Proof
- 86.6 percent context precision after rewriting and reranking; exact match fell on the full 918-query split
- Evidence
- Public evaluation harness
- Capability and evidence frontier
- Grounding improved while deterministic exact match declined, so the result is a tradeoff.
An evaluation harness comparing baseline and reranked retrieval-augmented generation pipelines with RAGAS and SQuAD metrics on the Mini Wikipedia corpus. The reranked pipeline reaches 86.6 percent context precision but loses exact match.
- Proof
- ROC AUC 0.757 on 185,000+ held-out classroom projects
- Evidence
- Public analysis repo
- Capability and evidence frontier
- The model is a policy triage aid; deployment as a funding decision system would require additional validation.
A model that flags DonorsChoose classroom requests most at risk of going unfunded, so limited reviewer attention can reach under-resourced schools first. The fairness audit reports unequal error rates across school poverty levels as part of the deployment analysis.
- Proof
- Six boundary self-checks gate CI: determinism, a k=5 distinct-text floor, a read-only SQL authorizer, redaction at rest, a hash-chained audit, and a private-table-free extract
- Evidence
- Public reproducible repo
- Capability and evidence frontier
- The controls stay structural rather than semantic; pattern redaction misses novel self-identifying text, and the floor counts distinct text rather than people.
A dependency-free prototype releases structure from a free-text SQLite dataset without exposing raw rows or private base tables. Pattern redaction cleans text before storage, a distinct-text floor of k=5 gates every public topic and aggregate cell, and SQLite's authorizer denies writes, private tables, and sensitive columns on each query.
- Proof
- Authorization, constraints, verification, and receipts for agent-run work
- Evidence
- Founder system, bounded public claims
- Capability and evidence frontier
- Public visuals explain the system model; live product claims require separate proof packets.
NUDG is a CMU AI Venture Studio project for controlling how agents use real resources. It replaces broad agent access with stepwise proposal, authorization, execution, verification, and receipt layers.
- Proof
- $10B+ mapped across 11 metros; Pittsburgh: $6.3B across 133 firms
- Evidence
- Restricted data, public aggregate summary
- Capability and evidence frontier
- Company-level records and maps stay private; the public page shows aggregates and methods evidence.
A Block Center project mapping more than ten billion dollars in public and private AI investment across eleven metropolitan economies for regional AI-readiness research. The Pittsburgh slice covers 6.3 billion dollars across 133 firms.
- Proof
- 14.3 percent vs 3.6 percent peak unemployment under paired policy regimes
- Evidence
- Public simulation repo
- Capability and evidence frontier
- Mechanism demonstration in a small simulated labor market, scoped away from macro forecasting.
An agent-based NetLogo model of a small labor market adjusting to AI automation. With identical workers, geography, and random seed, peak unemployment reaches 14.3 percent under a tech-driven policy regime versus 3.6 percent under a human-centric one.
- Proof
- Synthetic public sample with 7/7 privacy and integrity checks
- Evidence
- Private pilot, synthetic public artifact
- Capability and evidence frontier
- Public visuals use synthetic text so community messages stay private.
A civic-listening pilot with Professor Jordan Usdan of Heinz College that stores raw input privately and publishes privacy-checked extracts. The public synthetic sample demonstrates a 7/7 verification path while keeping community messages private.
- Proof
- Claude vision extracts flyer cards; backend-free static export runs with a sample board
- Evidence
- Live static demo
- Capability and evidence frontier
- The demo verifies the sample-board extraction workflow; live campus coverage remains outside scope.
A prototype that turns a photo of a campus poster wall into structured, personalized listings. Claude vision extracts one listing per flyer, a deterministic in-browser ranker orders the results by chosen interests, and the app ships as a backend-free static export.
- Proof
- Hour-by-hour cashflow model with thermostat, solar, and battery portfolio search
- Evidence
- Request-only capstone artifact
- Capability and evidence frontier
- The public artifact shows the function flow; full capstone materials are private.
A Streamlit planning tool for ERCOT demand response. It compares thermostat, solar, and battery portfolios hour by hour through benefit-cost cash-flow analysis.
- Proof
- Frozen-threshold holdout: recall 0.9000 (162 of 180 attacks; 95% CI 0.85 to 0.94) at FPR 0.0194, ROC AUC 0.9714, from an Isolation Forest fit on benign-only traffic
- Evidence
- Public reproducible repo + executed notebook
- Capability and evidence frontier
- Precision at the chosen threshold is 0.3189 at simulated 1% prevalence, the malicious class holds a single botnet-staging campaign on one honeypot host, and three dominant features leave the detector spoofable.
A Carnegie Mellon security-analytics project that fits an Isolation Forest on benign-only Linux kernel traffic from the BETH honeypot dataset, then grades it under a frozen threshold. On an 18,000-event holdout at simulated 1% attack prevalence, the detector catches 90% of intrusions (162 of 180) at a 1.94% false positive rate, with ROC AUC 0.9714. The entry audits its own headline: precision sits at 0.3189, so roughly one alert in three flags a real attack, and three dominant features hand an attacker a spoofing playbook, which keeps the detector one layer in defense in depth.
- Proof
- Across roughly ten tuned learners, random-CV R-squared 0.621 falls to 0.431 under spatial-block validation, and a leave-one-region-out spectrum sends tree-ensemble skill toward zero; Double ML estimates a +29.8% direct coastal premium (95% CI [0.16, 0.33] log points) and shrinks the naive private-school gap to +0.7%.
- Evidence
- Public reproducible repo + working paper
- Capability and evidence frontier
- Causal readings hold only under ignorability, the coastal estimate rests on 100 treated homes, and third-party terms keep home-level rows out of the repository, so a full rerun starts from the cleaned table the committed ingestion code rebuilds.
A University of Chicago Booth big-data study, co-authored with Will Sigal, that prices 3,804 Los Angeles County single-family homes from structural and neighborhood features, then audits its own headline. A tuned stacked ensemble reaches out-of-sample R-squared 0.621 under random cross-validation and falls to 0.431 when scoring moves to held-out geographic blocks; on the separate leave-one-region-out spectrum, tree-ensemble skill decays toward zero while the humble regularized linear model holds up best across space. Cross-fitted Double/Debiased ML puts the direct coastal premium near +29.8% per square foot, while the naive private-school premium collapses to a statistical zero.
- Proof
- In simulation, risk-aware MILP assignment with 240 agents (base risk weight alpha = 1) reached 94.8 percent on-time performance at 89.4 percent coverage, against a 48.8 percent observed baseline on the same synthetic delivery data.
- Evidence
- Public repository (committed notebooks and report)
- Capability and evidence frontier
- Every headline rate comes from simulation on a synthetic Kaggle delivery dataset; the report flags capacity calibration as the fragile assumption, and the dataset itself stays outside the public repository pending license review, with a README pointer to the download.
A predict-then-optimize framework for last-mile delivery, built with Santiago Enríquez as a balanced two-person team for Carnegie Mellon's 94-867 Data to Action course. Gradient-boosted models forecast a central estimate and an 80th-percentile bound for delivery time on a synthetic Kaggle dataset of Amazon-style delivery records; the triage MILP screens orders on the tail forecast, while the simulation MILP prices each order by a classifier's risk of missing its conformally set service promise. In day-by-day simulation, the risk-aware assignment reached 94.8 percent on-time performance at 89.4 percent coverage against a 48.8 percent observed baseline.
- Proof
- Across 3,183 headline words at 10 percent FDR, returns keep one survivor (damn, p = 1.0262e-05) versus 12 words for volatility; adding previous-day volatility lifts the lasso from zero selected words to in-sample R-squared 0.262.
- Evidence
- Public notebook repository
- Capability and evidence frontier
- In-sample evidence from one executed notebook on a dataset ending July 1, 2016; the source CSVs stay outside the public repo for license reasons, so a rerun starts from the Kaggle download plus the derived word files the README documents.
A Carnegie Mellon big-data coursework notebook that asks whether words in daily Reddit news headlines predict the Dow. Across 3,183 filtered words, marginal regressions with Benjamini-Hochberg FDR control leave exactly one return-predictive word, damn, a survivor the notebook reads as minimal predictive ability rather than discovery; volatility keeps 12 words and carries genuine persistence. Adding previous-day volatility lifts the lasso from zero selected words to an in-sample R-squared of 0.262, a compact lesson in multiple testing and confounding.
- Proof
- Adaptive poker bot with a documented study workflow: posterior opponent routing, packaged bucket-EV scoring, uncertainty-gated turn search, and manifest-driven duplicate-deck backtests with frozen release snapshots.
- Evidence
- Public submission repository
- Capability and evidence frontier
- Evidence rests on the committed bot and the documented method; the duplicate-deck study scripts that regenerate the comparisons stay outside the public tree, a public headline benchmark against the live tournament field remains pending, and the opponent models stay heuristic and statistical.
A tournament poker bot built for the CMU Data Science Club competition, aimed at decision-making under uncertainty against varied and adversarial opponents. The bot reads opponent tendencies from live betting signals, routes among specialist strategies through a bandit-style selector, and spends heavier turn-street search only on high-uncertainty spots. A manifest-driven backtest harness with duplicate-deck reconciliation and frozen release snapshots records every promotion decision against fixed opponents and score weights.