Thinking about becoming a statistician?
Wondering what it is a statistician even does?
Curious what you might end up studying as a graduate student in the field of statistics?
Statistics is everywhere, as witnessed by the interesting and exciting research projects underway at the University of Washington Statistics Department. Below you'll find some of the topics that graduate students and faculty are currently working on. Areas of work include mathematical statistics, environmental statistics, statistical genetics, social sciences, finance, and artificial intelligence. If you are preparing for undergraduate studies look at our ACMS Program. If you are thinking of graduate school take a look at some of the research projects described below, at our faculty home pages, and at the current research opportunities for our graduate students.
The NSF VIGRE program supports undergraduate and graduate students, postdocs who want to pursue carreers in statistics and mathematical sciences in creating an exciting research and learning environment.
Finally, read on to learn about what life could be like once you have your degree. Graduates from the Statistics Department at UW work in industry and academia in a variety of fields including business, physics, medicine, ecology, public policy, earth science, and biology.
Image Analysis of Gene Expression DataRaphael Gottardo, Julian Besag, Matthew Stephens, Alejandro Murua DNA microarrays are an increasingly important tool that allow biologists to gain insight into the function of thousands of genes in a single experiment. Image analysis is critical in interpreting the results of these experiments. Statisticians analyze images like the one at right and work on problems such as identifying the spot locations, classifying pixels as signal or background, and for each spot, estimating signal intensity and background intensity pairs. 

Visualizing Uncertainty in Weather PredictionVeronica Berrocal, J. McLean Sloughter, Michael Polakowski, Tilmann Gneiting, Adrian E. Raftery Current methods of weather prediction produce forecasts with unknown levels of uncertainty. Statisticians are working with atmospheric scientists and physicists to develop methods for estimating the uncertainty in weather predictions. They also work with psychologists to create tools for visualizing the uncertainty in these predictions and develop images like the one at left. 
Estimating the Ages of WhalesJudy Zeh Alaskan researchers hold a 13foot baleen plate from a bowhead whale. UW research professor Judy Zeh, Statistics, and graduate student Susan Lubetkin, Quantitative Ecology and Resource Management, are developing statistical models for estimating ages of the whales from measurements of stable isotope ratios in the baleen. 
A Semiparametric Regression Model for Panel Count DataJon A. Wellner, Ying Zhang, and Hao Liu. For a counting process with mean function conditional on a vector of covariates, we study the panel count data from the process. Our goal is to estimate the baseline mean function, and the vector of regression parameters. 
Forecasting Wind EnergyStatisticians working with the 3TIER Environmental Forecast Group Inc. are developing nextgeneration computer algorithms for forecasting the energy produced at large wind farms, like the one pictured at right. Current goals of the project focus on shortrange wind forecasting, a critical issue for both system operators and energy power marketers. More accurate wind forecasts translate into higher system reliability and lower costs. Later research will focus on improving wind forecasts at longer lead times, an issue that will be of increasing importance as wind energy production levels increase in future years, especially in regions with significant hydropower assets. 
Graphical Markov Models in Multivariate AnalysisMichael Perlman, Thomas Richardson, Sanjay Chaudhuri, Mathias Drton A central aspect of statistical science is the assessment of dependence among stochastic variables. The familiar concepts of correlation, regression, and prediction are special cases, and identification of causal relationships ultimately rests on representations of multivariate dependence. Graphical Markov models (GMM) use graphs, either undirected, directed, or mixed, to represent multivariate dependences in a visual and computationally efficient manner. A GMM is usually constructed by specifying local dependences for each variable, equivalently, node of the graph in terms of its immediate neighbors and/or parents by means of undirected and/or directed edges. This simple local specification can represent a highly varied and complex system of multivariate dependences by means of the global structure of the graph, thereby obtaining efficiency in modeling, inference, and probabilistic calculations. For a fixed graph, equivalently model, the classical methods of statistical inference may be utilized. In many applied domains, however, such as expert systems for medical diagnosis or weather forecasting, or the analysis of geneexpression data, the graph is unknown and is itself the first goal of the analysis. 
Modeling HIV and STDs in Drug User and Sexual NetworksInfectious diseases are distinguished from other diseases by being transmissible. Our understanding of disease transmission, and the preventive strategies that arise from such understanding, are therefore rooted in an implicit or explicit theory of population transmission dynamics. For infectious diseases like STDs and BBIs, that are only transmitted through the exchange of bodily fluids, the structure of the transmission network plays a particularly critical role. The epidemiology of these diseases  how quickly they spread and who gets infected  is driven by the network of persontoperson contact. Mathematical models of this process have provided a number of insights that have led to changes in STD control strategies. With the advent of HIV, however, new modeling challenges have emerged. In this research we develop new models for drug user and sexual networks as a means to understand the factors that influence the spread of HIV and other STDs. 
Separating Fractal Dimensions and Hurst EffectFractal behavior and longrange dependence have been observed in a large number of physical, biological, geological, and socioeconomic systems. Time series, profiles, and surfaces have been characterized by their fractal dimension, a measure of roughness, and by the Hurst coefficient, a measure of longmemory dependence. For selfsimilar processes, a linear relationship between fractal dimension and Hurst coefficient links local and global behavior. However, there are stochastic models that separate fractal dimension and Hurst Effect. In this display, the fractal dimension varies from left to right (D = 2.75, 2.5, 2) but is constant along columns. The Hurst coefficient varies from top to bottom (H = 0.9875, 0.9, 0.55) but is constant along rows. The images are simulated realizations of stationary Gaussian random fields, and were generated by the contributed package RandomFields within the R environment. 
Center for Statistics and the Social SciencesThere are a number of exciting research projects underway at the University of Washington Center for Statistics and the Social Sciences. Topics of some current projects include:

Where to go from here?
Statisticians work in a wide range of fields, applying their analytical skills to cuttingedge problems across many disciplines. The UW Statistics alumni page contains a list of over 100 former graduate students who have provided us with information. Also, take a look at some of the things that UW Statistics alumni are working on right now:
Academia 

Biology 

Business 

Consulting 

Earth Sciences 

Ecology 

Medicine 

Physics 

Public Policy 

Statistical Software 
