The
Department of Statistics: Past and Present
The
Department of Statistics of the University was established in 1949 to conduct
research into advanced statistics and probability, to work with others in the
application of statistics to investigations in the natural and social sciences,
and to teach probability and statistical theory and practice on the
undergraduate and graduate levels.
From
its beginning, the Department has been recognized for the high quality of its
faculty and the diversity of its interests. Some of the most important and
influential texts and monographs in statistics and probability of the past
forty years have been authored by former faculty members of our Department.
These include Ergodic Theory and Information, Convergence of
Probability Measures, and Probability
and Measure by Patrick Billingsley; Inference
and Disputed Authorship: The Federalist, an application of Bayesian methods
to fix the authorship of the Federalist Papers, by David L. Wallace and
Frederick Mosteller; and The Foundations of Statistics, a famous analysis of fundamental
problems by Leonard J. Savage. Current members of our faculty have written
definitive works in a variety of areas of current research interest. These
include Generalized Linear Models, an
influential monograph that extends the scope of linear models greatly,
including to models for discrete data, by Peter McCullagh
and John Nelder; Tensor
Methods in Statistics, a monograph on methods for making complex
multivariate calculations, by Peter McCullagh; Elements of Statistical Computing: Numerical
Computation, a far-ranging text on numerical methods for statistics by
Ronald A. Thisted; The History of Statistics: The Measurement of Uncertainty Before 1900,
and Statistics on the Table, accounts
by Stephen M. Stigler of the historical development of the field of
mathematical statistics; Interpolation of
Spatial Data: Some Theory for Kriging, a
monograph providing a sound mathematical basis for understanding the behavior
of a popular methodology for prediction of spatial processes by Michael L.
Stein; Michael J. Wichura recently published a
fundamental graduate text, The Coordinate
Free Approach to Linear Models; Lars Peter Hansen (with Thomas Sargent) recently published Robustness, an adaptation of robust control techniques to mis-specification problems in economics; Kirk Wolter's 2nd edition of his classic Introduction to Variance Estimation was recently issued; 2D Object Detection and Recognition, Models,
Algorithms, and Networks provides a state-of-the-art account of statistical
methods in computer vision by Yali Amit; and Greg Lawler's Random
Walk: A Modern Introduction has just been published.
Faculty
members have contributed many articles to books and journals in theoretical and
applied statistics, biophysics, chemistry, mathematics, geophysics, astronomy,
genetics, neuroscience, bacteriology, biometry, public health, machine
learning, artificial intelligence, imaging, psychology, sociology, medicine, law, history of science, education, and business. Members of
the department have at various times edited the four leading American or
international journals of probability and statistics. Steve Lalley
and Greg Lawler were previous editors of the Annals of Probability, the foremost research journal in the theory
of probability. Several faculty members have been president of one or both of
the two leading societies. Peter McCullagh, a leader
in the development of generalized linear models, is a Fellow of the Royal
Society. Stephen Stigler served recently as the President of the International
Statistical Institute and was recently appointed to the Royal Academy of
Belgium. Michael Stein is a leading expert in spatial and environmental
statistics. Per Mykland uses his expertise in
martingale theory and stochastic calculus to better understand financial
markets. Yali Amit is
developing fundamentally new approaches to computer vision and works on neural
models for memory and recognition. Mary Sara McPeek
studies genetic association and is a leader in understanding the statistical
impact of family relations between sampled individuals. Matthew Stephens studies genetic association,
sequencing and population genetics and is a leader in the application of modern
Bayesian methods in genetics. Dan Nicolae is co-chair
of a national asthma genetics consortium and studies genetic and environmental
factors affecting human disease.
Wei-Biao Wu is developing novel mathematical approaches to the analysis
of time series.
The
department is expanding its horizons into various fields of computational and
applied mathematics: John Reinitz, a leading mathematical
biologist studies mathematical models for the development of genetic
expression patterns, has joined our department. John Lafferty is a leader in
machine learning, studying the statistical properties of sparse models. We
welcome two new Professors in the computational and applied mathematics
initiative: Nicolas Brunel, a world leader in computational neuroscience
studying the dynamic properties of models for individual neurons and their
ensembles, and Mihai Anitescu,
a leader in non-convex constrained optimization.
Yali Amit
Chair |
