Paula Davis is a Managing Director at Deloitte, where she leads the Insight Studio. Established in 2012, the Insight Studio serves as a global center of excellence for data analytics and visualization at Deloitte. Paula works with clients to discover insights through analytics, answer data-driven questions through visualization, and gain perspective through design thinking. She has led multidisciplinary teams at the VizStudio to create over 300 analytical applications for various clients around the world to help them discover and pursue their newest opportunities. She has both an MBA and a BBA from the University of Georgia.
Marco Enriquez is a Senior Applied Mathematician in the Office of Data Science in the Division of Economic and Risk Analysis at the U.S. Securities & Exchange Commission (SEC). He is also a Senior Adjunct Professorial Lecturer in the Department of Mathematics and Statistics at American University. For the Spring 2020 semester, he taught a course on Natural Language Processing. His fields of Interest include Financial Reporting and Disclosure, Machine Learning, Big Data Analytics, and Natural Language Processing. He has a PhD in Computational and Applied Mathematics from Rice University, an MA in Computational and Applied Mathematics from Rice University, and a BS in Computer Engineering from Tufts University.
J. Alberto Espinosa is a Professor of Information Technology & Analytics at American University. His work studies the challenges, best practices, and most effective processes to coordinate technical work in complex work environments, such as across global boundaries; geographic distance and time zones; and large-scale, multifunctional collaboration. He employs multiple methods in his research, including theory, lab experiments, qualitative studies, and survey methods, but his primary focus is on field studies in technical organizations. His research has been published in leading scholarly journals, book chapters, and leading academic conference proceedings. A frequent presenter at academic conferences, Espinosa has several years of working experience as a design engineer and senior manager with international organizations. He has a PhD and an MS in Information Systems from Carnegie Mellon University, an MBA from Texas Tech University, and a BS in Mechanical Engineering from Pontificia Universidad Catolica del Peru.
Garrett Grolemund is a Principal Data Science Educator at RStudio, Inc. He is the author of Hands-On Programming with R and co-author of R for Data Science and R Markdown: The Definitive Guide. He has taught people how to use R at over 50 government agencies, small businesses, and multi-billion dollar global companies; and he's designed RStudio's training materials for R, Shiny, R Markdown and more. He wrote the lubridate R package and works for RStudio as both an educator and advocate of data science with R. Garrett was one of the earliest employees at RStudio and contributed to functions that have since matured into staffed Customer Success, Solutions Engineering, and Business Development roles. He has a PhD in Statistics from Rice University, an MA in Statistics from Harvard University, and a BA in Psychology from Harvard University.
Stas Kolenikov is a Principal Scientist at Abt Associates, with interests and expertise in social and behavioral sciences. In his position as a principal scientist, he deals with all possible issues in statistical methodology that are encountered in survey research and practice. He has developed sample frames and sampling designs for national and regional human population and establishment population surveys. Besides methodological aspects, he helps analysis workflows by incorporating reproducible research tools such as literate computing (markdown), source/version control, and project build/make procedures. Prior to joining Abt, he worked as an academic statistician and as a consultant. He has a PhD in Statistics from the University of North Carolina, Chapel Hill, an MA in Economics from the New Economic School, and an MEE in Engineering and Physics from the Moscow Institute of Electric Technology.
Monica Jackson is the American University Interim Deputy Provost and Dean of Faculty. Her areas of expertise include spatial statistics, statistical computation, and disease surveillance, where she is widely published in academic journals. She has presented for the National Cancer Institute and the US Census Bureau, as well as at numerous conferences. She has a PhD in Applied Mathematics and Scientific Computation from the University of Maryland, an MS in Applied Mathematics from Clark Atlanta University, and a BS in Mathematics from Clark Atlanta University.
Natalie Jackson is the Director of Research at the Public Religion Research Institute (PRRI), a nonprofit, nonpartisan organization dedicated to conducting independent research at the intersection of religion, culture, and public policy. She has held senior and management positions in media, academia, and nonprofit organizations. Most recently, she was the Managing Director of Polling at JUST Capital, where she built and managed a survey research team, as well as contributed to the overall mission and strategy of the nonprofit organization. Her work has appeared in peer-reviewed journals Electoral Studies and Social Science Quarterly, as well as in several edited volumes. Natalie received her PhD in political science from the University of Oklahoma and was a postdoctoral associate at the Duke University Initiative on Survey Methodology.
Rachel Shorey is a software engineer for The New York Times in the Interactive News department, where she works primarily on large data collections. She is also an instructor for Northwestern University’s Medill on the Hill program, which offers journalism students the opportunity to cover Congress, the White House, federal policy, and U.S. politics from a Washington newsroom. She has previously worked in software engineering and data science, including the Sunlight Foundation. Her research interests include the ethics of data science, teaching and learning coding skills and prime numbers. She has her undergraduate degree in Math and Linguistics from Swarthmore College and is a former PhD student of machine learning and natural language processing at the University of Massachusetts, Amherst.
Jane Wall is the Faculty Director of Data Science Programs at University of Colorado Boulder. She is a data scientist with a rare background combining business, communications and technical skills. She previously served as co-director of the Master of Science in Data Science at American University. She developed three of the core data science courses at AU and started the graduate certificate, BS and MS (with Jeff Gill) in Data Science. She worked for IBM and other software development companies as a software engineer, development manager, subcontract manager and account executive before getting her PhD. She has a PhD in Computational and Applied Mathematics from Rice University.