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  1. Matthew Stephens FRS (born 1970) is a Bayesian statistician and professor in the departments of human genetics and statistics at the University of Chicago. He is known for the Li and Stephens model as an efficient coalescent.

  2. News & Highlights. Matthew Stephens elected as a Fellow of the Royal Society!, May 10, 2023. Faculty accolade, December 30, 2019. Departments of Statistics and Human Genetics, and the College; Committee on Computational and Applied Mathematics (CCAM)

  3. codas.uchicago.edu › people › matthew-stephensMatthew Stephens | CDS

    Matthew Stephens. Chair, Department of Statistics; Ralph W. Gerard Professor of Statistics, Human Genetics, and the College. My lab works on a wide variety of problems at the interface of Statistics and Genetics. We often tackle problems where novel statistical methods are required, or can learn something new compared with existing approaches.

  4. May 22, 2023 · Matthew Stephens is a statistician and data scientist who has made groundbreaking contributions to the practice and applications of statistics in genetics. His research focuses on developing tractable approximations to complex inferential problems, combining innovative probabilistic models and computational methodology.

  5. Matthew Stephens is the Ralph W Gerard Professor of Statistics and Human Genetics at the University of Chicago, USA. He received a BA in Mathematics and Diploma in Mathematical Statistics from the University of Cambridge, and a D.Phil in Statistics from the University of Oxford.

  6. Stephens Lab Overview. My lab works on a wide variety of problems at the interface of Statistics and Genetics. We often tackle problems where novel statistical methods are required, or can learn something new compared with existing approaches.

  7. datascience.uchicago.edu › people › matthew-stephensMatthew Stephens | DSI

    Matthew Stephens. Chair, Department of Statistics; Ralph W. Gerard Professor of Statistics, Human Genetics, and the College. My lab works on a wide variety of problems at the interface of Statistics and Genetics. We often tackle problems where novel statistical methods are required, or can learn something new compared with existing approaches.