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UpStat 2019

April 26-27, 2019 at the University of Rochester, New York


EIGHTH ANNUAL CONFERENCE OF THE UPSTATE CHAPTERS OF THE AMERICAN STATISTICAL ASSOCIATION


Keynote Speaker
 Wendy Martinez

Dr. Wendy Martinez, US Bureau of Labor Statistics

Wendy Martinez has been serving as the Director of the Mathematical Statistics Research Center at the Bureau of Labor Statistics (BLS) for six years. Prior to this, she worked in several research positions throughout the Department of Defense. She held the position of Science and Technology Program Officer at the Office of Naval Research, where she established a research portfolio comprised of academia and industry performers developing data science products for the future Navy and Marine Corps. Her areas of interest include computational statistics, exploratory data analysis, and text data mining. She is the lead author of three books on MATLAB and statistics. These books cover topics ranging from classical approaches in statistics to computationally intensive methods and exploratory data analysis. She became interested in data science when pursuing her PhD at George Mason University, which she received in 1995. Dr. Martinez was elected as a Fellow of the American Statistical Association (ASA) in 2006 and is an elected member of the International Statistical Institute.  She was honored by the American Statistical Association when she received the ASA Founders Award at the JSM 2017 conference. Wendy is also proud and grateful to have been elected as the 2020 ASA President.

Saturday, April 27, 4:45 to 5:55 p.m.—Plenary Lecture 

Opportunities for Innovation in Data Science 

In this presentation, I will discuss several projects in statistics and data science that were conceived of and implemented by young researchers at the Bureau of Labor Statistics. Some examples of these projects include R Shiny apps for dynamic mapping of national employment statistics and the automatic generation of news releases, analyzing unstructured text from interviewer notes, and unsupervised learning (or clustering) of total survey error in employment statistics. As I go through these examples, I will highlight how these projects came about, the complexities associated with the data, and the innovative uses of statistics. I will conclude my talk with some information on careers in the Federal government and how to apply for them.

Keywords:  R Shiny, choropleth maps, text analysis, machine learning, clustering, careers