TUM – Technical University of Munich Menü

Prof. Dr. Mathias Drton

Department

Mathematics

Contact Details

Business card at TUMonline

Academic Career and Research Areas

Mathias Drton (*1975) works on methods and theory in the field of statistics. His main interest is the analysis of complex multivariate data. The main focus lies on graphical models, which capture fine causal relationships and find manifold applications in modern data-driven science. The aim of the research is, amongst others, to clarify by means of algebraic and probability theory under which circumstances data allow conclusions about causal relationships and to develop efficient methods for their estimation.

After studying in Augsburg, Mathias Drton received his doctorate in statistics in 2004 from the University of Washington, Seattle. He then worked as a postdoctoral fellow at the University of California, Berkeley. From 2005 to 2012 he was Assistant, Associate and Full Professor at the University of Chicago. In 2012 he became Professor of Statistics at the University of Washington. He spent the year 2018/19 at the University of Copenhagen. Since 2019 Mathias Drton is Professor of Mathematical Statistics at the TUM.

    Awards

    • Ethel Neubold Prize, Bernoulli Society (2019)
    • Elected Foreign Member of the Royal Danish Academy of Sciences and Letters (2018)
    • Elected Fellow, Institute of Mathematical Statistics (2016)
    • Medallion Lecture, Institute of Mathematical Statistics (2014)
    • Sloan Research Fellowship (2009)

    Key Publications

    Drton M, Fox C, Wang YS: „Computation of maximum likelihood estimates in cyclic structural equation models“. Annals of Statistics. 2019; 47(2): 663-690.

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    Drton M, Plummer M: „A Bayesian information criterion for singular models“. Journal of the Royal Statistical Society Series B. 2017; 79: 323-380.

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    Harris N, Drton M: „PC algorithm for Gaussian copula graphical models“. Journal of Machine Learning Research. 2013; 14: 3365-3383.

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    Foygel R, Draisma J, Drton M: „Half-trek criterion for generic identifiability of linear structural equation models“. Annals of Statistics. 2012; 40(3): 1682-1713.

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    Drton M, Sturmfels B, Sullivant S: Lectures on Algebraic Statistics. Oberwolfach Seminars. Basel: Birkhäuser Verlag, 2009.

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