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Charles Sutton is a research scientist at Google DeepMind and an honorary fellow of the School of Informatics at the University of Edinburgh. His research concerns probabilistic methods for machine learning, such as software engineering, natural language processing, computer security, and sustainable energy. He has published several papers on topics such as large language models, code generation, and variational inference.
Charles Sutton. I joined Google in January 2018. My research interests span deep learning, probabilistic machine learning, programming languages, data mining, and software engineering. I'm especially excited about applying deep learning to huge code bases, finding patterns about what makes for good code, leading to tools to help people write ...
Charles Sutton is a Research Scientist at Google Research. He is interested in a broad range of applications of machine learning, including NLP, analysis of computer systems, software engineering, and program synthesis. His work in software engineering has won an ACM Distinguished Paper Award.
Kexin Pei, David Bieber, Kensen Shi, Charles Sutton and Pengcheng Yin. In International Conference on Machine Learning. 2023. Identifying invariants is an important program analysis task with applications towards program understanding, bug finding, vulnerability analysis, and formal verification.
Charles Sutton, Timothy Hobson, James Geddes, Rich Caruana: Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling. KDD 2018 : 2279-2288
Biography. My research concerns a broad range of applications of probabilistic methods for machine learning, including software engineering, natural language processing, computer security, queueing theory, and sustainable energy.
