Guest Editor - Winter 2020
It is a very exciting time for scientists because new kinds of data—and a lot of it!—can help answer questions that were not previously answerable. This means statisticians get to play a central role in groundbreaking research in many different disciplines. New data sets and scientific questions often mean that we have to develop innovative new statistical methods and carefully study the properties of these methods to understand how well and when they work.
Innovations in statistics as well as computational methods often drive cutting-edge research, so it is crucial that research projects involve very close, long-term collaborations between statisticians and other scientists. This issue highlights some of the exciting new data-driven scientific research that is being carried out by interdisciplinary teams in the Eberly College of Science.
Penn State has been a pioneer in astrostatistics, a field that was advanced greatly starting in the 1980s by astronomer Eric Feigelson and statistician Jogesh Babu. They recognized that some of the great advances in astronomy—from discovering exoplanets to studying the evolution of the universe—would come from using and developing new statistical methods to extract information from the massive data sets that are available through modern technology like the Large Synoptic Survey Telescope (LSST) that is about to be launched in 2020. The challenges at the forefront of astrostatistics are immense, but Penn State faculty like Feigelson and Babu, as well as astronomer Eric Ford, astrostatistician Hyungsuk Tak, and others, have risen to meet them.
Another field where statistical methods have had an enormous impact is biology. A Penn State research team led by statisticians Francesca Chiaromonte and Matthew Reimherr and biologist Kateryna Makova is working to identify early childhood indicators of obesity risk. To do this well requires analyzing growth curves ("functions"), for which the statisticians have developed new "functional data analysis" methods. Statistical methods are also very important in public health, as in the work of statistics faculty member Le Bao, who has been working closely with international health organizations to study HIV/ AIDS, and that of statistics faculty member Daisy Philtron, who is developing statistical methods to identify genes involved in Parkinson's disease. While the statistical methods in all these projects have been developed in specific contexts, they typically have much wider applicability. For instance, Philtron's methodology may be applicable to understanding diseases other than Parkinson's.
In addition to all this exciting data- driven research, the Eberly College of Science and, more broadly, Penn State are investing heavily in developing new programs and research groups to help educate the next generation of statisticians and data scientists as well as data/statistics-savvy scientists. This issue spotlights statistics doctoral student Claire Kelling, who is also in the Social Data Analytics program, which brings together students and faculty who are interested in data-driven approaches to social science research. Data science is a newly ascendant discipline and already in great demand, even though there is yet to be consensus on specifically what it is. At Penn State, we have forged ahead with what we think is the best way to give our students a strong foundational education in data science. Our interdisciplinary undergraduate major in Data Sciences is hosted across three colleges: the Eberly College of Science, Information Science and Technology, and Engineering. We are proud to announce that our college just graduated its first data sciences students in May 2019.
These are undoubtedly exciting times for scientists and statisticians. What you will read in this issue is but a small glimpse of the many ways in which the Eberly College of Science is a particularly vibrant place for data- driven science and education.