In 2022 the American Statistical Association established the Riffenburgh Award, which recognizes exceptional innovation in extending statistical methods across diverse fields. Simultaneously, the Department of Statistics at the University of Connecticut proudly commemorated six decades of excellence, having evolved into a preeminent hub for academic, industrial, and governmental statistical grooming. To honor this legacy, a captivating virtual dialogue was conducted with the department’s visionary founder, Dr. Robert H. Riffenburgh, delving into his extraordinary career trajectory, profound insights into the statistical vocation, and heartfelt accounts from the faculty and students he personally nurtured. This multifaceted narrative documents the conversation with more detailed background information on each topic covered by the interview than what is presented in the video recording on YouTube.
Pub. online:22 Feb 2021Type:Data Science In Action
Journal:Journal of Data Science
Volume 19, Issue 2 (2021): Special issue: Continued Data Science Contributions to COVID-19 Pandemic, pp. 334–347
Abstract
Coronavirus and the COVID-19 pandemic have substantially altered the ways in which people learn, interact, and discover information. In the absence of everyday in-person interaction, how do people self-educate while living in isolation during such times? More specifically, do communities emerge in Google search trends related to coronavirus? Using a suite of network and community detection algorithms, we scrape and mine all Google search trends in America related to an initial search for “coronavirus,” starting with the first Google search on the term (January 16, 2020) to recently (August 11, 2020). Results indicate a near-constant shift in the structure of how people educate themselves on coronavirus. Queries in the earliest days focusing on “Wuhan” and “China”, then shift to “stimulus checks” at the height of the virus in the U.S., and finally shift to queries related to local surges of new cases in later days. A few communities emerge surrounding terms more overtly related to coronavirus (e.g., “cases”, “symptoms”, etc.). Yet, given the shift in related Google queries and the broader information environment, clear community structure for the full search space does not emerge.