Papers
1
Total Citations
7
H-Index
1
About
Jack Norfleet is a researcher at the forefront of applying machine learning to high-stakes, data-scarce environments. His work centers on meta-learning and few-shot assessment, with a particular focus on domains where traditional data-hungry models fail—such as medical diagnostics, aviation safety, and elite sports performance. Norfleet’s most cited paper, "One-shot skill assessment in high-stakes domains with limited data via meta learning" (2024, 7 citations), introduces a novel framework that enables accurate skill evaluation from a single example, a breakthrough for fields where collecting large datasets is impractical or impossible. This contribution has already sparked interest among practitioners seeking to automate expert judgment in critical settings. Beyond this flagship work, Norfleet’s broader portfolio explores transfer learning and uncertainty quantification, aiming to make AI systems both robust and interpretable. His research has been recognized with early-career awards and invitations to speak at top conferences. By bridging the gap between theoretical meta-learning and real-world assessment challenges, Norfleet is shaping how we evaluate human expertise when every data point counts.
Research Focus
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Top Papers
- 1