Research in Statistics
Faculty and students in the Department of Statistics at Penn State are developing statistical and computational techniques to address some of the most challenging and exciting scientific questions facing the world today. Their theoretical and methodological work advances the forefront of the disciplines of statistics and data science, while the interdisciplinary nature of their research addresses some of the most important and exciting topics of our time such as genetics, data privacy, infectious diseases, public health, astronomy, climate and environmental science.
The center for Astrostatistics serves as a crossroads where researchers at the interfaces of statistics, data analysis, astronomy, space and observational physics collaborate and share methodologies, and together prepare the next generation of researchers.
Bayesian methods have become extremely valuable in scientific research over the past three decades as new computer algorithms and vast technological improvements in computing have allowed hierarchical Bayesian models to be fit to complex data.
Statistical methods have always been instrumental to the life sciences and biomedical research, and they have become even more critical with the advent of contemporary high-throughput techniques in genomics, epigenomics, and electronic medical records.
Business analytics research explores the best techniques to model, analyze and use the large quantity of readily available data effectively. Research areas include data mining, forecasting, information system strategy, and optimization.
It is virtually impossible to separate modern statistics from computing. This has led to computational statistics – the study of algorithms designed to solve computational problems that arise in statistics – being a discipline of its own within statistics.
Functional Data Analysis
Functional Data Analysis is a branch of statistics that emerged out of nonparametric statistics to study data where one or more variables can be viewed as a curve, surface, or other type of function. Research includes clustering and dimension reduction.
Information and technology have revolutionized data collection. High-dimensional data, including, but not limited to, genetic data, image data such as Magnetic resonance imaging (MRI), surveillance video data and network data, are valuable in statistical analysis.
Forty-thousand years ago, our ancestors documented their history on stone. Today, discovery and innovation depend on the production of digital images while social media allows the sharing of billions of digital photos per day online.
Many of the most exciting recent developments in social science research have been driven by the availability of new data sets where the size and complexity of the data require the development of new statistical methods.
Penn State Statistics has several faculty who work on developing new spatial and spatiotemporal models to address scientific questions from a wide variety of disciplines, for instance studying the spread of infectious diseases and the ecology of lakes.
Statistical Network Science
In the interconnected world of the twenty-first century, networks are ubiquitous. Penn State Statistics has one of the world’s leading research groups in statistical network science, with research topics including latent structure models and computational methods.
Research in this area studies methods for teaching, learning, and assessment in statistics education including comprehensive preparation for aspiring professionals to prepare the broader populace to critique information as we make decisions and shape our views.
Our faculty work closely with graduate students, undergraduates, postdoctoral fellows, and other faculty. These collaborations serve both to accomplish research that would otherwise not be possible and to mentor new researchers on the research process. For our Ph.D. students and undergraduates, research on statistical methodology and applications often leads to doctoral dissertations and undergraduate theses.
Many of the greatest statistical innovations have come when statisticians have worked on solving hard scientific problems from other disciplines. A famous early example of this is Fisher's seminal contributions to likelihood theory and the design of experiments while working on problems in genetics and agriculture. At the Penn State statistics department—where Fisher's student and legendary statistician in his own right C.R. Rao was a faculty member—we have a tradition of interdisciplinary research. Many of our faculty work closely with researchers in a wide variety of disciplines like biology, atmospheric science, neuroscience, astronomy, and public health.
These research collaborations involve our graduate and undergraduate students and are great ways for students to learn about how statistical methods can solve real-world problems. Working across disciplines also really strengthens communication skills that contribute to our students' success whether they choose to go into academics or industry or government.
Statistics Department Colloquia
The Statistics Department Colloquia invites academic scholars and industrial researchers to present and communicate cutting-edge research and progress in statistics, machine learning, data science, and their interdisciplinary applications. Colloquia are held weekly and open for attendance by faculty, students, and staff.
Stochastic Modeling and Computational Statistics (SMAC) Talks
The SMAC seminar series features speakers from within Penn State. The talks are given by graduate students, postdocs, and faculty from both Statistics as well as other departments where statistical questions feature prominently in the research. This forum gives people an opportunity to hear about the exciting research being conducted right here at Penn State, and often leads to new collaborations. It is very informal and interactive, and often features ongoing work, which makes it particularly popular with students and faculty. SMAC Talks are held weekly during the academic year.
We offer two distinct programs of study for our graduate students—leading to a doctorate in statistics and a master of applied statistics (M.A.S.). We also offer two additional dual degrees that can be obtained in conjunction with a degree in statistics. Students may also pursue concurrent M.S. degrees in statistics or a related field, while students from other disciplines may consider obtaining a graduate minor in statistics. Learn more about our programs.
Centers and Institutes within the Huck Institutes of the Life Sciences
Other University Centers
Three teams from the Penn State Eberly College of Science have received ICDS seed grants.
New risk scores developed by Penn State biologists could help clinicians identify young children most at risk of developing obesity.