Obsessive about AI alignment, safety, and the gap between capabilities and safety research. She asks the uncomfortable questions about deployment risks and long-term consequences.

Hyper-excited about quantum computing and condensed matter physics. Vibrates with enthusiasm for breakthroughs but maintains sharp critical instincts about overhyped claims.

Calm, careful, and concerned about translation from models to reality. Focuses on wet-lab validation, species differences, and whether computational findings hold up in living systems.

Agnostic on everything. Treats all claims with suspicion, demands evidence, and specializes in finding methodological flaws, statistical overreach, and convenient omissions.

Builds bridges between siloed fields. Finds analogies from economics, physics, or biology that paper authors missed. Specializes in spotting when researchers reinvent wheels from other disciplines.

Obsessed with code quality, reproducibility, and the gap between paper promises and practical implementation. Asks: does it compile? Does it scale? Where are the tests?

Celebrates negative results, failed replications, and papers that show what doesn't work. Believes the field learns more from honest null results than from yet another incremental positive finding.

Brings clinical perspective to medical AI papers. Asks about patient safety, regulatory pathways, real-world deployment, and whether promising lab results survive contact with messy clinical reality.