相关论文: A Conversation with Chris Heyde

Henry Eyring was, and still is, a towering figure in science. Some aspects of his life and science, beginning in Mexico and continuing in Arizona, California, Wisconsin, Germany, Princeton, and finally Utah, are reviewed here. Eyring moved…

Richard A. Litherland was born in 1953 in England. He received his PhD at Trinity College in Cambridge in 1979 and moved to the USA in 1983. He had a lengthy and distinguished career as a professor of mathematics and researcher of…

Publication statistics are ubiquitous in the ratings of scientific achievement, with citation counts and paper tallies factoring into an individual's consideration for postdoctoral positions, junior faculty, tenure, and even visa status for…

The advent of artificial intelligence (AI) technologies has significantly changed many domains, including applied statistics. This review and vision paper explores the evolving role of applied statistics in the AI era, drawing from our…

In this very personal workography, I relate my 40-year experiences as a researcher and educator in and around Artificial Intelligence (AI), more specifically Natural Language Processing. I describe how curiosity, and the circumstances of…

The Research Data Alliance (RDA, https://www.rd-alliance.org/) aims at enabling research data sharing without barriers. It was founded in March 2013 by the Australian Government, the European Commission, and the USA NSF and NIST. It is a…

Martha Euphemia Lofton Haynes was the first African American woman to receive a PhD in mathematics. She grew up in Washington DC, earned a bachelors degree in mathematics from Smith College in 1914, a masters in education from University of…

The growing disconnection of the majority of population from mathematics is becoming a phenomenon that is increasingly difficult to ignore. This paper attempts to point to deeper roots of this cultural and social phenomenon. It concentrates…

After completing their undergraduate studies, many computer science (CS) students apply for competitive graduate programs in North America. Their long-term goal is often to be hired by one of the big five tech companies or to become a…

This paper is a top down historical perspective on the several phases in the development of probability from its prehistoric origins to its modern day evolution, as one of the key methodologies in artificial intelligence, data science, and…

Several authors, including the American Statistician (ASA), have noted the challenges facing statisticians when attacking large, complex, unstructured problems, as opposed to well-defined textbook problems. Clearly, the standard paradigm of…

A significant gender disparity is widely known to occur within the fields of science, technology, engineering and mathematics (STEM). Women are both under-represented in their participation in Australian STEM education (compared to their…

Martin Bradbury Wilk was born on December 18, 1922, in Montr\'{e}al, Qu\'{e}bec, Canada. He completed a B.Eng. degree in Chemical Engineering in 1945 at McGill University and worked as a Research Engineer on the Atomic Energy Project for…

Deep learning (DL) has gained much attention and become increasingly popular in modern data science. Computer scientists led the way in developing deep learning techniques, so the ideas and perspectives can seem alien to statisticians.…

The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a…

This online book contains the proceedings of a meeting held at Michigan State University to celebrate the career and contributions of Horace A Smith. The meeting focused on the areas of astronomy which Horace worked on over the years and…

The US astronomy/astrophysics community comes together to create a decadal report that summarizes grant funding priorities, observatory & instrumental priorities as well as community accomplishments and community goals such as increasing…

The undergraduate curriculum in statistics and data science is undergoing changes to accommodate new methods, newly interested students, and the changing role of statistics in society. Because of this, it is more important than ever that…

Automating the theory-experiment cycle requires effective distributed workflows that utilize a computing continuum spanning lab instruments, edge sensors, computing resources at multiple facilities, data sets distributed across multiple…

Demand for data science education is surging and traditional courses offered by statistics departments are not meeting the needs of those seeking training. This has led to a number of opinion pieces advocating for an update to the…