Scholar
Clayton Sanford
Google Scholar ID: Qo18yHAAAAAJ
Google Research
Citations & Impact
Citations
874
H-index
10
i10-index
11
Publications
20
Co-authors
7
list available
Publications
Resume (English only)
Academic Achievements
- Research primarily consists of mathematical results about the capabilities and limitations of neural networks and other machine learning algorithms, including random feature models, deep neural networks, and self-attention units; recognized as an Outstanding Intern at the Allen Institute for AI for contributions to climate modeling; served as a reviewer for ICLR 2024, NeurIPS 2023, and other conferences.
Research Experience
- Currently a Research Scientist at Google, focusing on LLM data and modeling; interned at LinkedIn (data science), Lumi Labs (engineering), Allen Institute for AI (climate modeling), Microsoft Research (transformer theory), and Google Research (transformers and graph reasoning).
Education
- Completed a Ph.D. in Computer Science in 2024 from Columbia University under the supervision of Rocco Servedio and Daniel Hsu; studied applied math and computer science as an undergraduate at Brown University.
Background
- Research interests lie at the intersection of machine learning and theoretical computer science, particularly in improving the interpretability, transparency, and accountability of neural networks by understanding their mathematical properties. Funded by an NSF GRFP fellowship during his Ph.D. research.
Miscellany
- Primarily codes in Python and is experienced with core data science and deep learning packages such as Pytorch, Tensorflow, Sklearn, and Pandas; TA'd five different courses at Brown and coordinated undergraduate seminars at Columbia; contributed to publicly available code repositories.
