Cornell University · B.A. Computer Science
Building reproducible evaluation and interpretability systems for transparent, reliable AI.
Triangulating AI Reliability: Evidence, Mechanisms, and Human Judgment
Outlines my direction toward a reliability layer for AI safety, integrating evidence-grounded evaluation, mechanistic interpretability, and human/rubric adjudication.
Conducting multi-lab research on evaluation, interpretability, and human-aligned reliability systems.
Cornell University
Long-Term AI Safety Research Lab (Lionel Levine) — Interpretability reliability and meta-probing infrastructure; developing evaluation methods for model understanding tools.
Future of Learning Lab (René Kizilcec) — Rubric-anchored evaluation pipelines and dialogic feedback systems for reliable AI in education.
AI & Robotics Lab (Angelique Taylor) — Systematic review of multi-agent reinforcement learning applications in healthcare.
Culture & Computation Lab (David Mimno & Mathew Wilkens) — Representation alignment and literary corpus analysis using multimodal embeddings.
Former: Horizon Therapeutics · Stripe · Discovery Partners Institute (Alvin Chin)
I’m a researcher focused on developing auditable, evidence-based evaluation systems that make model reliability measurable. My goal is to bridge mechanistic interpretability, evidence-grounded NLP, and human-aligned evaluation to build transparent AI pipelines ready for oversight and safety-critical domains.
Last updated: October 2025
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