I build machine learning systems for markets, graphs, and simulation.

First-authored an accepted ICML 2026 DEMO workshop paper arguing that effective sample size is not a universal uncertainty diagnostic for off-policy evaluation, and that CI width and empirical coverage are stronger cross-estimator signals. Built reward-noise and distribution-shift stress tests that isolate where effective sample size stays stable even as estimator variance and absolute error climb.

First-authored Stigmergic Graph Memory (SGM), a paper under review at AAAI-27 that reframes endpoint selection in many-to-many warehouse pickup and delivery as a congestion-control decision, not just a routing one. SGM is a bounded, decaying graph-memory layer that reads recent execution signals on warehouse nodes and directed edges to rank which source–destination pairs enter the planner, while a feasibility proof keeps every plan collision-free. Across paired benchmarks on five layouts, three fleet sizes, and 25 seeds, it beats two reconstructed many-to-many baselines in all 15 conditions, with throughput gains of 20.5–36.7%.

Built a Swift-to-AWS Amplify sync layer for continuous mobile-health data collection, raising background-collection uptime by over 150% while cutting daily battery use by roughly 65%. Wrote Python and TensorFlow pipelines that process 200,000+ daily multimodal data points into participant-level time-series features for downstream clinical AI research.