Researcher and data scientist working at the intersection of economics, data science, and AI evaluation. I bring an economist's causal-inference toolkit to questions about how AI systems behave and what we can reliably learn from large-scale data — dual graduate training in Economics (Iowa State) and Information/Data Science (Michigan).

Running an ongoing weekly LLM calibration & uncertainty evaluation program — original evaluations of whether frontier models represent their own uncertainty, spanning probabilistic forecast calibration, interval calibration as question specificity decreases, and cross-level belief consistency. Scored with proper scoring rules (CRPS, log loss, pinball), ECE, and ground-truth-free self-consistency checks; power-analyzed rather than anecdotal (effect sizes, scenarios-needed-for-80%-power, multiple-comparison caveats). Across the program so far: frontier models stay overconfident on rare/underspecified cases, and self-reported confidence is largely uninformative. The anchoring arm closed at Round 10, and the finding it ended on wasn't the anchoring effect itself but a same-turn contradiction — models answer "greater" on turn one, then emit an interval lying entirely on the other side of the anchor. Whether clearer labels fix it turns out to be model-specific, token priming was ruled out with a matched control, and the Round 10 confirmation was predicted in a git-committed file before the runs and held on a new item set.

An ongoing weekly program on whether LLMs can represent their own uncertainty. Each round reuses the previous round's design or data — newest first.

Round 10 was pre-registered — the prediction sits in commit f09a6c2, committed before the confirmatory runs, and it held.

Side branch (R4) — same harness, different question: model output quality and judge bias rather than uncertainty. haiku-evals (5-7-5 form + subject grounding via embedding similarity) → haiku-judge-evals (blind pairwise judging — does a model pick its own haiku more often than an independent judge would?)

• plagiarism-detection-ir — multi-signal (BM25 + SBERT + Jaccard) reuse detection, PAN 2011 benchmark
• sandhill-crane-migration-networks — max-flow min-cut migration bottlenecks over 3.2M observations
• ebird-species-prediction — collaborative-filtering and multi-task models over 13.6B observations (SLURM/HPC)

• Research integrity & reproducibility — independent statistical replications and forensic data analysis at Cornell supporting institutional research-integrity investigations; co-launched a cross-disciplinary reproducibility initiative, independently reproducing 15+ peer-reviewed studies across economics, sociology, communication, and veterinary medicine; presented methodology at IASSIST 2019.
• Large-scale applied ML — billion-observation HPC pipelines (SLURM, Great Lakes cluster) for ecological modeling: collaborative-filtering and multi-task neural models over 13.6B observations, directed-network migration bottleneck analysis via max-flow min-cut over 3.2M observations.
• Information retrieval — multi-signal (BM25 + SBERT + Jaccard) plagiarism/reuse detection pipeline evaluated on the PAN 2011 benchmark, addressing a gap standard tools like Turnitin miss.
• Agentic systems — technical lead on an LLM-powered natural-language-to-simulation-code interface for a biological cell-modeling platform (Compucell3D / UMich).