Statistics

University of California, Berkeley

This is an archived copy of the 2014-15 guide. To access the most recent version of the guide, please visit http://guide.berkeley.edu/.

Overview

The Department of Statistics grants BA, MA, and PhD degrees in Statistics. The undergraduate and graduate programs allow students to participate in a field that is growing in breadth of application and importance. Understanding the natural and human worlds in the "information age" increasingly requires statistical reasoning and methods, and stochastic models are essential components of research and applications across a vast spectrum of fields. The Department of Statistics provides students with world-class resources for study and research, including access to the extensive computational facilities maintained by the Statistical Computing Facility.

Facilities and Resources

The Statistical Computing Facility (SCF) is a unit of the Department of Statistics. Its mission is to provide the undergraduate students, graduate students, postdocs, and faculty in the Statistics Department at Berkeley with state-of-the-art computing resources, services, and technical knowledge, supporting them in carrying out cutting-edge research activities, innovative instructional programs, and efficient day-to-day computing activities. The SCF also supports the students and faculty of the Econometrics Laboratory of the Department of Economics.

The Department of Statistics operates a consulting service in which advanced graduate students, under faculty supervision, are available as consultants during specified hours. The service is associated with the course STAT 272 Statistical Consulting, which may be taken for credit. Consulting is free to members of the campus community. Statistical advice can be sought at any stage of the research process. Those seeking statistical advice are encouraged to contact consultants early in the research process. Refer to the Department of Statistics website  to find out which faculty member is currently coordinating this service.

Three seminars regularly take place in the Department : the Neyman seminar ,  the probability seminar , and the statistics and genomics seminar . Each year, we also have two joint seminars with Stanford and a joint seminar with Davis.

Undergraduate Programs

Statistics : BA

Graduate Programs

Statistics : MA, PhD

Visit Department Website

Courses

Statistics

STAT 0PX Preparatory Statistics 1 Unit

This course assists entering Freshman students with basic statistical concepts and problem solving. Designed for students who do not meet the prerequisites for 2. Offered through the Student Learning Center.

STAT 2 Introduction to Statistics 4 Units

Population and variables. Standard measures of location, spread and association. Normal approximation. Regression. Probability and sampling. Binomial distribution. Interval estimation. Some standard significance tests.

STAT 20 Introduction to Probability and Statistics 4 Units

For students with mathematical background who wish to acquire basic concepts. Relative frequencies, discrete probability, random variables, expectation. Testing hypotheses. Estimation. Illustrations from various fields.

STAT 21 Introductory Probability and Statistics for Business 4 Units

Descriptive statistics, probability models and related concepts, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.

STAT W21 Introductory Probability and Statistics for Business 4 Units

Reasoning and fallacies, descriptive statistics, probability models and related concepts, combinatorics, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.

STAT 39D Freshman/Sophomore Seminar 2 - 4 Units

Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester.

STAT C79 Societal Risks and the Law 3 Units

Defining, perceiving, quantifying and measuring risk; identifying risks and estimating their importance; determining whether laws and regulations can protect us from these risks; examining how well existing laws work and how they could be improved; evaluting costs and benefits. Applications may vary by term. This course cannot be used to complete engineering unit or technical elective requirements for students in the College of Engineering.

STAT 94 Special Topics in Probability and Statistics 1 - 4 Units

Topics will vary semester to semester.

STAT 97 Field Study in Statistics 1 - 3 Units

Supervised experience relevant to specific aspects of statistics in off-campus settings. Individual and/or group meetings with faculty.

STAT 98 Directed Group Study 1 - 3 Units

Must be taken at the same time as either Statistics 2 or 21. This course assists lower division statistics students with structured problem solving, interpretation and making conclusions.

STAT 100 Introduction to the SAS System for Data Analysis 1 Unit

The SAS system is useful for reading input data from a variety of sources and then performing a wide range of analyses and graphical displays with the data. Topics include accessing SAS on a variety of computer platforms; inputting raw data; managing SAS data sets; programming in SAS and in the SAS macro language. Emphasis on large data sets. Students are encouraged to bring in their own data. Students should have used at least one program, such as a word processor.

STAT 131A Introduction to Probability and Statistics for Life Scientists 4 Units

Ideas for estimation and hypothesis testing basic to applications, including an introduction to probability. Linear estimation and normal regression theory.

STAT 132 Practical Machine Learning 3 Units

Machine learning is a collection of topics in which the focus is on large-scale statistical problems where computational issues are paramount. The goal is often one of prediction or classification, where based on a set of labeled data it is desired to predict the lablels of unlabeled data. Machine learning algorithms also often focus on exploratory data analysis. This course will introduce core statistical machine learning algorithms in a non-mathematical way, emphasizing applied problem-solving.

STAT 133 Concepts in Computing with Data 3 Units

An introduction to computationally intensive applied statistics. Topics will include organization and use of databases, visualization and graphics, statistical learning and data mining, model validation procedures, and the presentation of results.

STAT 134 Concepts of Probability 3 Units

An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.

STAT 135 Concepts of Statistics 4 Units

A comprehensive survey course in statistical theory and methodology. Topics include descriptive statistics, maximum likelihood estimation, non-parametric methods, introduction to optimality, goodness-of-fit tests, analysis of variance, bootstrap and computer-intensive methods and least squares estimation. The laboratory includes computer-based data-analytic applications to science and engineering.

STAT 150 Stochastic Processes 3 Units

Random walks, discrete time Markov chains, Poisson processes. Further topics such as: continuous time Markov chains, queueing theory, point processes, branching processes, renewal theory, stationary processes, Gaussian processes.

STAT 151A Linear Modelling: Theory and Applications 4 Units

A coordinated treatment of linear and generalized linear models and their application. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies.

STAT 151B Linear Modelling: Theory and Applications 4 Units

A coordinated treatment of linear and generalized linear models and their application. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies.

STAT 152 Sampling Surveys 4 Units

Theory and practice of sampling from finite populations. Simple random, stratified, cluster, and double sampling. Sampling with unequal probabilities. Properties of various estimators including ratio, regression, and difference estimators. Error estimation for complex samples.

STAT 153 Introduction to Time Series 4 Units

An introduction to time series analysis in the time domain and spectral domain. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra.

STAT 154 Modern Statistical Prediction and Machine Learning 4 Units

Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.

STAT 155 Game Theory 3 Units

General theory of zero-sum, two-person games, including games in extensive form and continuous games, and illustrated by detailed study of examples.

STAT 157 Seminar on Topics in Probability and Statistics 3 Units

Substantial student participation required. The topics to be covered each semester that the course may be offered will be announced by the middle of the preceding semester; see departmental bulletins. Recent topics include: Bayesian statistics, statistics and finance, random matrix theory, high-dimensional statistics.

STAT 158 The Design and Analysis of Experiments 4 Units

An introduction to the design and analysis of experiments. This course covers planning, conducting, and analyzing statistically designed experiments with an emphasis on hands-on experience. Standard designs studied include factorial designs, block designs, latin square designs, and repeated measures designs. Other topics covered include the principles of design, randomization, ANOVA, response surface methodoloy, and computer experiments.

STAT 159 Reproducible and Collaborative Statistical Data Science 4 Units

A project-based introduction to statistical data analysis. Through case studies, computer laboratories, and a term project, students will learn practical techniques and tools for producing statistically sound and appropriate, reproducible, and verifiable computational answers to scientific questions. Course emphasizes version control, testing, process automation, code review, and collaborative programming. Software tools may include Bash, Git, Python, and LaTeX.

STAT H195 Special Study for Honors Candidates 1 - 4 Units

STAT 197 Field Study in Statistics 1 - 3 Units

Supervised experience relevant to specific aspects of statistics in off-campus settings. Individual and/or group meetings with faculty.

STAT 198 Directed Study for Undergraduates 1 - 3 Units

Special tutorial or seminar on selected topics.

STAT 199 Supervised Independent Study and Research 1 - 3 Units

STAT 200A Introduction to Probability and Statistics at an Advanced Level 4 Units

Probability spaces, random variables, distributions in probability and statistics, central limit theorem, Poisson processes, transformations involving random variables, estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.

STAT 200B Introduction to Probability and Statistics at an Advanced Level 4 Units

Probability spaces, random variables, distributions in probability and statistics, central limit theorem, Poisson processes, transformations involving random variables, estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.

STAT 201A Introduction to Probability at an Advanced Level 4 Units

Distributions in probability and statistics, central limit theorem, Poisson processes, modes of convergence, transformations involving random variables.

STAT 201B Introduction to Statistics at an Advanced Level 4 Units

Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.

STAT 204 Probability for Applications 4 Units

A treatment of ideas and techniques most commonly found in the applications of probability: Gaussian and Poisson processes, limit theorems, large deviation principles, information, Markov chains and Markov chain Monte Carlo, martingales, Brownian motion and diffusion.

STAT C205A Probability Theory 4 Units

The course is designed as a sequence with Statistics C205B/Mathematics C218B with the following combined syllabus. Measure theory concepts needed for probability. Expection, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations, martingales and martingale convergence theorems. Markov chains. Stationary processes. Brownian motion.

STAT C205B Probability Theory 4 Units

The course is designed as a sequence with with Statistics C205A/Mathematics C218A with the following combined syllabus. Measure theory concepts needed for probability. Expection, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations, martingales and martingale convergence theorems. Markov chains. Stationary processes. Brownian motion.

STAT C206A Advanced Topics in Probability and Stochastic Process 3 Units

The topics of this course change each semester, and multiple sections may be offered. Advanced topics in probability offered according to students demand and faculty availability.

STAT C206B Advanced Topics in Probability and Stochastic Processes 3 Units

The topics of this course change each semester, and multiple sections may be offered. Advanced topics in probability offered according to students demand and faculty availability.

STAT 210A Theoretical Statistics 4 Units

An introduction to mathematical statistics, covering both frequentist and Bayesian aspects of modeling, inference, and decision-making. Topics include statistical decision theory; point estimation; minimax and admissibility; Bayesian methods; exponential families; hypothesis testing; confidence intervals; small and large sample theory; and M-estimation.

STAT 210B Theoretical Statistics 4 Units

Introduction to modern theory of statistics; empirical processes, influence functions, M-estimation, U and V statistics and associated stochastic decompositions; non-parametric function estimation and associated minimax theory; semiparametric models; Monte Carlo methods and bootstrap methods; distributionfree and equivariant procedures; topics in machine learning. Topics covered may vary with instructor.

STAT 212A Topics in Theoretical Statistics 3 Units

This course introduces the student to topics of current research interest in theoretical statistics. Recent topics include information theory, multivariate analysis and random matrix theory, high-dimensional inference. Typical topics have been model selection; empirical and point processes; the bootstrap, stochastic search, and Monte Carlo integration; information theory and statistics; semi- and non-parametric modeling; time series and survival analysis.

STAT 212B Topics in Theoretical Statistics 3 Units

This course introduces the student to topics of current research interest in theoretical statistics. Recent topics include information theory, multivariate analysis and random matrix theory, high-dimensional inference. Typical topics have been model selection; empirical and point processes; the bootstrap, stochastic search, and Monte Carlo integration; information theory and statistics; semi- and non-parametric modeling; time series and survival analysis.

STAT 215A Statistical Models: Theory and Application 4 Units

Applied statistics with a focus on critical thinking, reasoning skills, and techniques. Hands-on-experience with solving real data problems with high-level programming languages such as R. Emphasis on examining the assumptions behind standard statistical models and methods. Exploratory data analysis (e.g., graphical data summaries, PCAs, clustering analysis). Model formulation, fitting, and validation and testing. Linear regression and generalizations (e.g., GLMs, ridge regression, lasso).

STAT 215B Statistical Models: Theory and Application 4 Units

Course builds on 215A in developing critical thinking skills and the techniques of advanced applied statistics. Particular topics vary with instructor. Examples of possible topics include planning and design of experiments, ANOVA and random effects models, splines, classification, spatial statistics, categorical data analysis, survival analysis, and multivariate analysis.

STAT 222 Masters of Statistics Capstone Project 4 Units

The capstone project is part of the masters degree program in statistics. Students engage in professionally-oriented group research under the supervision of a research advisor. The research synthesizes the statistical, computational, economic, and social issues involved in solving complex real-world problems.

STAT 230A Linear Models 4 Units

Theory of least squares estimation, interval estimation, and tests under the general linear fixed effects model with normally distributed errors. Large sample theory for non-normal linear models. Two and higher way layouts, residual analysis. Effects of departures from the underlying assumptions. Robust alternatives to least squares.

STAT 232 Experimental Design 4 Units

Randomization, blocking, factorial design, confounding, fractional replication, response surface methodology, optimal design. Applications.

STAT 239A The Statistics of Causal Inference in the Social Science 4 Units

Approaches to causal inference using the potential outcomes framework. Covers observational studies with and without ignorable treatment assignment, randomized experiments with and without noncompliance, instrumental variables, regression discontinuity, sensitivity analysis and randomization inference. Applications are drawn from a variety of fields including political science, economics, sociology, public health and medicine.

STAT 239B Quantitative Methodology in the Social Sciences Seminar 4 Units

A seminar on successful research designs and a forum for students to discuss the research methods needed in their own work, supplemented by lectures on relevant statistical and computational topics such as matching methods, instrumental variables, regression discontinuity, and Bayesian, maximum likelihood and robust estimation. Applications are drawn from political science, economics, sociology, and public health. Experience with R is assumed.

STAT C239A The Statistics of Causal Inference in the Social Science 4 Units

Approaches to causal inference using the potential outcomes framework. Covers observational studies with and without ignorable treatment assignment, randomized experiments with and without noncompliance, instrumental variables, regression discontinuity, sensitivity analysis and randomization inference. Applications are drawn from a variety of fields including political science, economics, sociology, public health and medicine.

STAT 240 Nonparametric and Robust Methods 4 Units

Standard nonparametric tests and confidence intervals for continuous and categorical data; nonparametric estimation of quantiles; robust estimation of location and scale parameters. Efficiency comparison with the classical procedures.

STAT C241A Statistical Learning Theory 3 Units

Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods.

STAT C241B Advanced Topics in Learning and Decision Making 3 Units

Recent topics include: Graphical models and approximate inference algorithms. Markov chain Monte Carlo, mean field and probability propagation methods. Model selection and stochastic realization. Bayesian information theoretic and structural risk minimization approaches. Markov decision processes and partially observable Markov decision processes. Reinforcement learning.

STAT 243 Introduction to Statistical Computing 4 Units

Concepts in statistical programming and statistical computation, including programming principles, data and text manipulation, parallel processing, simulation, numerical linear algebra, and optimization.

STAT 244 Statistical Computing 4 Units

Algorithms in statistical computing: random number generation, generating other distributions, random sampling and permutations. Matrix computations in linear models. Non-linear optimization with applications to statistical procedures. Other topics of current interest, such as issues of efficiency, and use of graphics.

STAT C245A Introduction to Modern Biostatistical Theory and Practice 4 Units

Course covers major topics in general statistical theory, with a focus on statistical methods in epidemiology. The course provides a broad theoretical framework for understanding the properties of commonly-used and more advanced methods. Emphasis is on estimation in nonparametric models in the context of contingency tables, regression (e.g., linear, logistic), density estimation and more. Topics include maximum likelihood and loss-based estimation, asymptotic linearity/normality, the delta method, bootstrapping, machine learning, targeted maximum likelihood estimation. Comprehension of broad concepts is the main goal, but practical implementation in R is also emphasized. Basic knowledge of probability/statistics and calculus are assume

STAT C245B Biostatistical Methods: Survival Analysis and Causality 4 Units

Analysis of survival time data using parametric and non-parametric models, hypothesis testing, and methods for analyzing censored (partially observed) data with covariates. Topics include marginal estimation of a survival function, estimation of a generalized multivariate linear regression model (allowing missing covariates and/or outcomes), estimation of a multiplicative intensity model (such as Cox proportional hazards model) and estimation of causal parameters assuming marginal structural models. General theory for developing locally efficient estimators of the parameters of interest in censored data models. Computing techniques, numerical methods, simulation and general implementation of biostatistical analysis techniques with emphasis on data applications.

STAT C245C Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine 4 Units

This course provides an introduction to computational statistics, with emphasis on statistical methods and software for addressing high-dimensional inference problems in biology and medicine. Topics include numerical and graphical data summaries, loss-based estimation (regression, classification, density estimation), smoothing, EM algorithm, Markov chain Monte-Carlo, clustering, multiple testing, resampling, hidden Markov models, in silico experiments.

STAT C245D Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine II 4 Units

This course and Pb Hlth C240C/STAT C245C provide an introduction to computational statistics with emphasis on statistical methods and software for addressing high-dimensional inference problems that arise in current biological and medical research. The courses also discusses statistical computing resources, with emphasis on the R language and environment (www.r-project.org). Programming topics to be discussed include: data structures, functions, statistical models, graphical procedures, designing an R package, object-oriented programming, inter-system interfaces. The statistical and computational methods are motivated by and illustrated on data structures that arise in current high-dimensional inference problems in biology and medicine.

STAT C245E Statistical Genomics 4 Units

Genomics is one of the fundamental areas of research in the biological sciences and is rapidly becoming one of the most important application areas in statistics. This is the first course of a two-semester sequence, which provides an introduction to statistical and computational methods for the analysis of meiosis, population genetics, and genetic mapping. The second course is Statistics C245F/Public Health C240F. The courses are primarily intended for graduate students and advanced undergraduate students from the mathematical sciences.

STAT C245F Statistical Genomics 4 Units

Genomics is one of the fundamental areas of research in the biological sciences and is rapidly becoming one of the most important application areas in statistics. The first course in this two-semester sequence is Public Health C240E/Statistics C245E. This is the second course, which focuses on sequence analysis, phylogenetics, and high-throughput microarray and sequencing gene expression experiments. The courses are primarily intended for graduate students and advanced undergraduate students from the mathematical sciences.

STAT C247C Longitudinal Data Analysis 4 Units

The course covers the statistical issues surrounding estimation of effects using data on subjects followed through time. The course emphasizes a regression model approach and discusses disease incidence modeling and both continuous outcome data/linear models and longitudinal extensions to nonlinear models (e.g., logistic and Poisson). The primary focus is from the analysis side, but mathematical intuition behind the procedures will also be discussed. The statistical/mathematical material includes some survival analysis, linear models, logistic and Poisson regression, and matrix algebra for statistics. The course will conclude with an introduction to recently developed causal regression techniques (e.g., marginal structural models). Time permitting, serially correlated data on ecological units will also be discussed.

STAT 248 Analysis of Time Series 4 Units

Frequency-based techniques of time series analysis, spectral theory, linear filters, estimation of spectra, estimation of transfer functions, design, system identification, vector-valued stationary processes, model building.

STAT C249A Censored Longitudinal Data and Causality 4 Units

This course examines optimal robust methods for statistical inference regarding causal and non-causal parameters based on longitudinal data in the presence of informative censoring and informative confounding of treatment. Models presented include multivariate regression models, multiplicative intensity models for counting processes, and causal models such as marginal structural models and structural nested models. Methods will be illustrated with data sets of practical interest and analyzed in the laboratory section. This course, appropriate for advanced masters and Ph.D. students, provides exposure to a number of ongoing research topics.

STAT 259 Reproducible and Collaborative Statistical Data Science 4 Units

A project-based introduction to statistical data analysis. Through case studies, computer laboratories, and a term project, students will learn practical techniques and tools for producing statistically sound and appropriate, reproducible, and verifiable computational answers to scientific questions. Course emphasizes version control, testing, process automation, code review, and collaborative programming. Software tools may include Bash, Git, Python, and LaTeX.

STAT 260 Topics in Probability and Statistics 3 Units

Special topics in probability and statistics offered according to student demand and faculty availability.

STAT C261 Quantitative/Statistical Research Methods in Social Sciences 3 Units

Selected topics in quantitative/statistical methods of research in the social sciences and particularly in sociology. Possible topics include: analysis of qualitative/categorical data; loglinear models and latent-structure analysis; the analysis of cross-classified data having ordered and unordered categories; measure, models, and graphical displays in the analysis of cross-classified data; correspondence analysis, association analysis, and related methods of data analysis.

STAT 272 Statistical Consulting 3 Units

To be taken concurrently with service as a consultant in the department's drop-in consulting service. Participants will work on problems arising in the service and will discuss general ways of handling such problems. There will be working sessions with researchers in substantive fields and occasional lectures on consulting.

STAT 278B Statistics Research Seminar 1 - 4 Units

Special topics, by means of lectures and informational conferences.

STAT 298 Directed Study for Graduate Students 1 - 12 Units

Special tutorial or seminar on selected topics.

STAT 299 Individual Study Leading to Higher Degrees 1 - 12 Units

Individual study

STAT 375 Professional Preparation: Teaching of Probability and Statistics 2 - 4 Units

Discussion, problem review and development, guidance of laboratory classes, course development, supervised practice teaching.

STAT 601 Individual Study for Master's Candidates 1 - 8 Units

Individual study in consultation with the graduate adviser, intended to provide an opportunity for qualified students to prepare themselves for the master's comprehensive examinations. Units may not be used to meet either unit or residence requirements for a master's degree.

STAT 602 Individual Study for Doctoral Candidates 1 - 8 Units

Individual study in consultation with the graduate adviser, intended to provide an opportunity for qualified students to prepare themselves for certain examinations required of candidates for the Ph.D. degree.

Faculty

Professors

Peter L Bartlett, Professor. Statistics, machine learning, statistical learning theory, adaptive control.
Research Profile

David R. Brillinger, PhD, Professor. Risk analysis, statistical methods, data analysis, animal and fish motion trajectories, statistical applications in engineering and science, sports statistics.
Research Profile

Steven N. Evans, Professor. Genetics, random matrices, superprocesses & other measure-valued processes, probability on algebraic structures -particularly local fields, applications of stochastic processes to biodemography, mathematical finance, phylogenetics & historical linguistics.
Research Profile

Michael J. Klass, Professor. Statistics, mathematics, probability theory, combinatorics independent random variables, iterated logarithm, tail probabilities, functions of sums.
Research Profile

Elchanan Mossel, Professor. Applied probability, statistics, mathematics, finite markov chains, markov random fields, phlylogeny.
Research Profile

James W Pitman, Professor. Fragmentation, statistics, mathematics, Brownian motion, distribution theory, path transformations, stochastic processes, local time, excursions, random trees, random partitions, processes of coalescence.
Research Profile

Philip B. Stark, Professor. Astrophysics, law, statistics, litigation, causal inference, inverse problems, geophysics, elections, uncertainty quantification, educational technology.
Research Profile

Bin Yu, Professor. Neuroscience, remote sensing, networks, statistical machine learning, high-dimensional inference, massive data problems, document summarization.
Research Profile

Associate Professors

Noureddine El Karoui, Associate Professor. Applied statistics, theory and applications of random matrices, large dimensional covariance estimation and properties of covariance matrices, connections with mathematical finance.
Research Profile

Haiyan Huang, Associate Professor. Applied statistics, functional genomics, translational bioinformatics, high dimensional and integrative genomic/genetic data analysis, network modeling, hierarchical multi-lable classification.
Research Profile

Assistant Professors

Adityanand Guntuboyina, Assistant Professor.

Elizabeth Purdom, PhD, Assistant Professor. Computational biology, bioinformatics, statistics, data analysis.
Research Profile

Benjamin Recht, Assistant Professor.

Allan Sly, Assistant Professor.

Adjunct Faculty

Lisa Robin Goldberg, PhD, Adjunct Faculty.

Jon Mcauliffe, Adjunct Faculty. Bioinformatics, machine learning, nonparametrics, convex optimization, statistical computing, prediction, supervised learning.
Research Profile

Lecturers

Ani Adhikari, Lecturer.

Fletcher H Ibser, Lecturer.

Contact Information

Department of Statistics

367 Evans Hall

Phone: 510-642-2781

Fax: 510-641-7892

Visit Department Website

Department Chair

Philip B. Stark, PhD

403 Evans Hall

Phone: 510-394-5077

stark@stat.berkeley.edu

Undergraduate Program Committee Chair

Ani Adhikari, PhD

413 Evans Hall

Phone: 510-642-2208

ani@stat.berkeley.edu

Undergraduate Student Affairs Officer

Denise Yee

367 Evans Hall

Phone: 510-643-6131

dyee@berkeley.edu

Graduate Student Services Adviser

La Shana Porlaris

373 Evans Hall

Phone: 510-642-5361

lashana@berkeley.edu

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