发布时间：2014-05-26
时间：5月27日15:30
地点：25-3B
Statistics, Computer and Big Data
Dennis K.J. Lin
Penn State University, USA
Classical statistical inference, which based upon limited samples to inference the entire population, is now challenged by the new era of data-rich environment—in some cases, the entire population data is available! After noting the relative absence of statisticians from the community of practice engaged with big data, in this talk we explain what big data is, how it's done, and who's working with it. Statisticians have much more to contribute in both the intellectual vitality and the practical utility of big data. At the same time, big data challenges statisticians to move out of some familiar habits to engage less structured problems, to become more comfortable with ambiguity, and to engage computer scientists in a more fruitful discussion of what the various parties can bring to this new mode of investigation. At the end of this talk, we propose some potential directions for future research.
Bio.
Dr. Dennis Lin is a University Distinguished Professor of Supply Chain and Statistics at Penn State University. His research interests are quality assurance, industrial statistics, data mining and Statistical Inference. He has published near 200 professional (SCI/SSCI) papers in a wide variety of prestigious journals (such as, Technometrics, Annals of Statistics, Biometrika, Statistica Sinica, etc). He has served as a co-editor for ASMBI as well as an associate editor for various (about 10) top journals. Dr. Lin is an elected fellow of ASA, IMS and ASQ, an elected member of ISI, a fellow of RSS, and a lifetime member of ICSA. He is the recipient of the 2004 Faculty Scholar Medal Award at Penn State University. He is also an honorary chair professor for various universities, including a Chang-Jiang Scholar at Renmin University of China. His recent awards include Don Owen Award (ASA), Youden Address (ASQ) and Loutit Lecturer (SSC).
