Round Table 1: Data Science
John Bailer (Miami University in Ohio, USA).
Bio: John Bailer is university distinguished professor and chair in the Department of Statistics at Miami University in
Southwest Ohio. After earning undergraduate degrees from Miami University, he received a Ph.D. in biostatistics from the University of North Carolina at
Chapel Hill. Work as an National Institute of Health (NIH) staff fellow preceded his return to Miami in 1988 as a faculty member. He is the
founding chair of the Department of Statistics at Miami, and he is currently in his 13th year in that role.
His research interests include quantitative risk estimation, the design and analysis of environmental toxicology & occupational health studies
studies and gerontological data analysis. He has published 150+ peer-reviewed papers, many with student co-authors, 4 books and 40+ other publications.
He has taught 28 different courses since arriving at Miami including a few that he designed. He developed the statistical programming course and the
advanced data visualization courses offered in the department. He has mentored over 50 graduate student projects and served on another 100
graduate student committees.
Promoting quantitative literacy and enhancing connections between statistics and journalism are more recent passions which resulted in
the Stats+Stories podcast that he developed with journalism colleagues. He is currently finishing a book, Statistics Behind the Headlines, that
he is co-authoring with a journalism colleague - a perfect gift for someone you love. He is most happy when hanging out with his
kids (one now in Chicago, another in London and two local), walking his dog, reading fun fiction, and pre-pandemic, traveling internationally. .
Luciano Rebouças de Oliveira (Federal University of Bahia (UFBA), Brazil).
Bio: PhD in Electrical and Computer Engineering, at the Institute of Systems and Robotics, University of Coimbra, M.Sc. in Mechatronics
and Bachelor in Computer Science at the Federal University of Bahia (UFBA). Associate Professor at the Dept. of Computer Science, at Institute of Computing,
UFBA, and head of the Intelligent Vision Research Lab. Specialist in the field of Computer Vision and Machine Learning, working mainly in robotics,
smart cities, biometric systems and biomedicine.
Pedro Luis do Nascimento Silva (National School of Statistical Sciences (ENCE), Brazil).
Bio: Pedro Luis do Nascimento Silva holds a PhD. in Social Statistics (University of Southampton, 1996). He is the Secretary of
SCIENCE (since 2019), a Retired Principal Researcher from the National School of Statistical Sciences (ENCE) of the Brazilian Institute for Geography and
Statistics (IBGE), and was the President of the International Statistical Institute between 2015 and 2017. His main research interests include survey and
sampling methodology applied to household and business surveys, as well as the analysis of survey data.
Round Table 2: The future of statistical education
Denise Silva (National School of Statistical Sciences (ENCE), Brazil).
Bio: Principal Researcher of the National School of Statistical Sciences (ENCE) from the Brazilian Institute of Geography and Statistics (IBGE).
She completed her PhD in Statistics at the University of Southampton and has been working as a survey methodologist for more than 30 years as well as a
lecture at graduate and undergraduate levels. Denise was president (2019-2021) of the International Association of Survey Statisticians (IASS),
is an elected member of the International Statistical Institute (ISI) and is an editor of the Statistical Journal of the IAOS and the International
Statistical Review. Her main areas of interest are survey methods, official/public statistics, statistical modelling for social sciences, small area
estimation and time series analysis.
Nalini Ravishanker (University of Connecticut, USA).
Bio: Nalini Ravishanker is professor in the Department of Statistics at the University of Connecticut (UConn), Storrs.
Her current research interests include time series and times-to-events analysis, Bayesian dynamic modeling, signal processing, and predictive inference.
Her primary interdisciplinary research involves problems in finance, marketing, and transportation engineering. She has an undergraduate degree in
statistics from Presidency College, Chennai, India, and a PhD in statistics and operations research from the Stern School of Business,
New York University. She has over 100 publications, has co-authored a textbook A First Course in Linear Model Theory (second edition),
and is co-editor of the Handbook of Discrete-Valued Time Series, both published by Chapman & Hall/CRC. She is a fellow of the
American Statistical Association, an elected member of the International Statistical Institute, and an elected member of the CT Academy
of Science and Engineering. She served as President of the International Society for Business and Industrial Statistics (ISBIS) 2017-2019
and as the VP for Education Outreach for the New England Statistics Society (NESS) 2017-2020. She has served as the theory and methods
editor of Applied Stochastic Models in Business and Industry, and as an associate editor for The American Statistician. She is
currently co-editor-in-chief of the International Statistical Review and an associate editor for the Journal of Forecasting,
Chilean Journal of Statistics and Annals of Science and Technology.
Marcos Magalhães (University of São Paulo, Brazil).
Bio: Marcos Nascimento Magalhães – Professor do Departamento de Estatística do Instituto de Matemática e Estatística da Universidade de
São Paulo (IME-USP). Licenciado e Mestre em Estatística pelo IME-USP e Doutor em Engenharia Industrial e Pesquisa Operacional pela Virginia Polytechnic
Institute and State University, Virginia, EUA. Sua área de pesquisa é Probabilidade Aplicada e Educação Estatística.