CATS, established in 1978, promotes the statistical sciences, statistical education, statistics applications, and related issues affecting the statistics community. The mission and scope of CATS evolved over time as interdisciplinary collaboration increasingly shaped the character of scientific research. After a brief hiatus, CATS was reconstituted in 2011 and has since focused on improving the visibility and practice of statistics within government agencies not well connected to statistics, increasing attention to statistical issues of big data and data science, and helping agencies identify bottlenecks impairing their analysis capabilities. Its multidisciplinary members are experts from statistics and related fields and leaders in diverse areas of interdisciplinary research, including the analysis of large-scale data, computational biology and bioinformatics, spatial data, environmental science, neuroscience, health care policy, and complex computer experiments.
The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The nation’s ability to make use of these data depends on the availability of an educated workforce with necessary expertise. With these new capabilities have come novel ethical challenges regarding the effectiveness and appropriateness of broad applications of data analyses.
On Thursday and Friday, October 27-28, 2016, the Office of Financial Research and the University of Michigan’s Center on Finance, Law and Policy hosted a joint conference, “Big Data in Finance” in Ann Arbor, Michigan, which brought together a wide range of scholars, regulators, policymakers, and practitioners to explore how big data can be used to enhance financial stability and address other challenges in financial markets. More than 250 people attended from around the United States and abroad.
SIAM Journal on Mathematics of Data Science (SIMODS) publishes work that advances mathematical, statistical, and computational methods in the context of data and information sciences. We invite papers that present significant advances in this context, including applications to science, engineering, business, and medicine.
From July 7 to July 12 the IEEE International Symposium on Information Theory will take place in Paris France. Al Hero is General Co-Chair of the conference. The conference website is https://2019.ieee-isit.org/.