Samuel Stevens

Samuel Stevens works on computer vision and machine learning, with a focus on building AI models that can recognize and classify biological species (such as animals and plants) from images, including in data-scarce or long-tailed settings. A related thread of his work involves making these AI systems more interpretable, developing methods to understand what visual and language models have learned internally. He also has some work on cryptographic problem-solving (Learning With Errors) using machine learning techniques.

Publication output has grown noticeably in recent years, rising from occasional papers before 2022 to a peak of 8 in 2025.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026