Statistics

Research in the department is wide ranging, both in terms of areas of applications and in terms of focus, from questions very closely related to a particular type of data (e.g normalization of microarrays) to theoretical questions at the intersection of mathematical statistics and modern probability theory.

You will find here a high-level overview of our current interests split, for the convenience of the reader, between

 

Applied Statistics

The information technology revolution has advanced data collection capacities in almost all fields of science, social science, engineering, and beyond. The resulting data abundance provides extremely fertile grounds for applied statistics. On one hand, the IT age applied statistics research is challenged by the massiveness of data which makes the fastest computer seem slow and data visualization difficult. On other hand, applied statistics research is also given an unprecedented opportunity to impact old and new fields outside statistics.

Our faculty's applied statistics research spans a wide range of such fields including astronomy, geophysics, remote sensing, AIDS research, genetics and bioinformatics, neuroscience, transportation, computer science, information and data compression, the census, demography and law, the theory of options pricing, and financial statistics. 

Specifically, our interdisciplinary research can be grouped into four categories:

Physical Science:

  • Animal Movement Study (David Brillinger)
  • Astrophysics (Philip Stark)
  • Astronomy (John Rice)
  • Bioinformatics/Computational Biology (Peter Bickel, Sandrine Dudoit, Haiyan Huang, Michael Jordan, Nicholas P. Jewell, Elizabeth Purdom, Juliet P. Shaffer, Terry Speed)
  • Environment Risk Analysis (David Brillinger, Mark van der Laan)
  • Neuroscience (David Brillinger, Bin Yu)
  • Genetics (Steven Evans, Terry Speed)
  • Geophysics (Philip Stark)
  • Phylogenetic Trees (Steve Evans, Elchanan Mossel)
  • Seismology (David Brillinger)

Social Science:

  • Educational Technology (Philip Stark, Deborah Nolan)
  • Educational Statistics (Juliet P. Shaffer),
  • Federal Statistical System (Kenneth Wachter)
  • Law and Statistics (Philip Stark)
  • Teaching of Statistics (Deborah Nolan)

Engineering:

  • Artificial Intelligence (Michael Jordan)
  • Computer Vision (Peter Bickel)
  • Program Debugging (Michael Jordan)
  • Network Tomography (Bin Yu)
  • Remote Sensing (Bin Yu)
  • Signal/Image Processing (Martin Wainwright, Bin Yu)
  • Text Mining (Michael Jordan)
  • Transportation Modeling (Peter Bickel) and applications of functional data analysis and time series analysis to (John Rice)

Other areas:

  • Epidemiology (particularly of infectious diseases, including AIDS and SARS) (Nicholas P. Jewell)
  • Finance (Steven Evans, Noureddine El Karoui)
  • Medical Research (Mark van der Laan)

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Biostatistics and Bioinformatics

The Department of Statistics has had long-standing interests in the development and application of statistical methods to address problems in biological and medical research. A particularly important current area of research concerns the analysis of genomic data from high-throughput microarray and sequencing assays (Peter Bickel, Sandrine Dudoit, Steve Evans, Haiyan Huang, Mike Jordan, Rasmus Nielsen, Elizabeth Purdom, Yun Song, Terry Speed). Other areas of interest include clinical trials, epidemiology, and survival data analysis (Mark van der Laan, Nick Jewell). 

Research in these areas is motivated by close and active collaborations with biologists and clinicians on the Berkeley campus, at nearby institutions (e.g., Lawrence Berkeley National Laboratory, Children's Hospital Oakland Research Institute, University of California, San Francisco, and Stanford University), at local biotech companies (e.g., Genentech, Genomic Health, Veracyte, with whom with also have ties through our Industry Alliance Program), or as part of international consortia such as ENCODE  and TCGA. The faculty have also recently established a Biomedical Statistics Research Group (BSR) in the Li Ka Shing Center (LKS), to develop new collaborations related to the LKS core areas (i.e., cancer biology, infectious diseases, neurodegenerative diseases, stem cell biology).

Faculty are members of the Graduate Group in Biostatistics and of the Center for Computational Biology and advise PhD students for the Designated Emphasis in Computational and Genomic Biology. Some of the faculty are also involved in the newly-created PhD Program in Computational Biology.

 

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Theoretical statistics

The theoretical research interests of the department focus on the mathematical foundations of data analysis, including time series analysis, pattern recognition and classification, nonparametric methods, survival analysis, information theory, asymptotic approximations, experimental design, causal inference, and graphical models for complex dependencies.

Methods for high dimensional data and machine learning

Faculty associated with the Designated Emphasis in Communication, Computation and Statistics work in this area. Peter Bartlett, Peter Bickel and Bin Yu have been working on theoretical analyses of "boosting" from both the statistics and computer science point of view. Michael Jordan and Martin Wainwright have been working on the construction of effective algorithms. Noureddine El Karoui and Peter Bickel are working on the asymptotic behavior of empirical covariance matrices as dimension and sample size becomes large. Juliet Shaffer works on multiple testing such as the False Discovery Rate.

Causal and graphical models

Mark van der Laan has recently published a book (with J. Robins) on causal modelling. Michael Jordan is completing a major treatise on graphical modelling. Martin Wainwright is working on the analysis of the Junction Tree Algorithm.

Time Series and Survival Analysis

David Brillinger has long worked on general modelling and analysis methods for time series as well as modifying the models effectively for applications in many fields, most recently environmental science and neuroscience. John Rice works on the theory and methods for analysis of data represented as functions with a view towards various applications. Nicholas Jewell and Mark van der Laan work on the modelling of complex types of data arising in clinical trials.

Classical Statistics

Ching Shui Cheng works on the deep algebraic and combinatorial aspects of experimental design. Leo Goodman works on methods for the analysis of discrete data such as the relation between log linear latent variable models and correspondence analysis. Philip Stark works on minimax problems in decision theory. Deborah Nolan works on empirical process theory. David Freedman and Peter Bickel have helped develop the theory of the bootstrap. Freedman made significant contributions to the analysis of Bayes' procedures in high dimensional spaces.

Demographics and Phylogenetics

Kenneth Wachter works on demographic models, particularly biodemography, and the study of ageing. Steven Evans works on models in phylogenetics and population genetics.

Information Theory

Bin Yu and Martin Wainwright work on the functions of statistics and information theory, such as Rissanen's MDL model selection method.

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