Summary
Stanford professor and AI researcher. Known for foundational work on meta-learning (MAML). Co-author of Meta-Harness, applying automated search to optimize LLM harness configurations.
Key Points
- Stanford CS faculty, leads research on meta-learning, robotics, and few-shot learning
- MAML (Model-Agnostic Meta-Learning) is one of the most cited papers in the field
- Meta-Harness work extends the meta-learning intuition to harness design — learning how to configure systems rather than learning task solutions directly
Evidence Timeline
- 2026-04-07: Co-author of Meta-Harness paper (arXiv:2603.28052)