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• Leakage-safe temporal evaluation

  What “available at forecast time” really means for train, validation, and test splits, and how easy it is to cheat by accident.

• Post-processing operational forecasts by lead time

  Why learn a correction on top of NextGen reforecasts instead of replacing the model, and what changes between 1 and 18 hours ahead.

• Sequence models for hydrology: LSTM, Transformer, Mamba

  What a fair comparison between recurrent, attention, and state-space models needs before anyone declares a winner.

• AI and agentic workflows in earth-science research

  What I am putting in front of scientists at AGU26: what is worth adopting, and what to be cautious about.

• Reproducible research pipelines

  Reforecast generation you can rerun, versioned data lineage, and metrics by site and lead time instead of one aggregate score.

• From production software to research code

  What transfers from GraphQL services at USAA to research code, and what had to be unlearned.

• Bounding LLM autonomy in decision systems

  Why LLMs should advise rather than act, and how Harmony confines them to nodes that can flag or veto while every consequential action waits for a human.

• Reliability lessons from executive-missions operations

  What 50+ executive airlift missions, including Air Force Two, with zero safety-related incidents taught me about preparation and reliability, and how that shows up in software.

• From military service into AI

  Why I chose to pursue AI while serving, and what the path from an Air Force flying job to computer science and graduate AI research looked like, for veterans weighing the same move.

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