KEYNOTE SPEAKERS
Katherine D. Ensor (Rice University, USA; Vice-President of the American Statistical Association).
Title: The Data Science Expert in the Room
Abstract: In today’s data driven world, data scientists and statisticians are in high demand. We have a wide range of
methods and tools available to answer key questions across the spectrum of human inquiry. Through our methodological training we are also
able to expand upon our core expertise to widen this set of tools when necessary. In this talk, I will tell the story of the importance of
stepping up and serving as the data science expert in the room, and finding the right tools for the questions asked. The background of the
story is the devastation that Hurricane Harvey caused to Houston, Texas in August 2017. At the same time that we were helping our neighbors,
the scientists in the Houston area quickly moved to action to bring their expertise to the challenges the disaster brought. As a data
scientist and a leading scholar in Houston, I have a seat at the table to help with Houston’s recovery and reconstruction as the region
moves forward. From a data collection perspective, the Kinder Urban Data Platform served as an expeditious way to integrate the real time
data processes and to fully understand the longterm human health impact we established the Harvey Registry. Our wide range of data science
tools and expertise is indispensable as the community transitions data to knowledge and action. For example, understanding the environmental
and housing impact requires integration of spatially referenced data through advanced spatial statistics. Finally, for the bigger issues of
changing flood patterns, methods in spatial-temporal extremes are necessary to address the key questions put forward by the hydrologists
and city planners with whom I work. In each case, it was critical to the timely success of the project that I stepped up to serve as the
data science expert and to execute the research on a time scale that met the needs of the team. I offer that our methodological training
supports us all in serving the public good and humanitarian causes.
Hedibert Lopes (Insper, Brazil).
Title: Efficient sampling for Gaussian linear regression with arbitrary priors
Abstract: This paper develops a slice sampler for Bayesian linear regression models with
arbitrary priors. The new sampler has two advantages over current approaches. One, it is faster than many
custom implementations that rely on auxiliary latent variables, if the number of regressors is large. Two, it
can be used with any prior with a density function that can be evaluated up to a normalizing constant, making it ideal
for investigating the properties of new shrinkage priors without having to develop custom sampling algorithms. The new sampler
takes advantage of the special structure of the linear regression likelihood, allowing it to produce better effective sample size
per second than common alternative approaches. (joint work with P. Richard Hahn and Jingyu He).
Dani Gamerman (Federal University of Rio de Janeiro, Brazil).
Title: Being an applied statistican/Como ser um estatístico aplicado
Abstract: This talk illustrates different forms one can be an applied statistician,
emphasizing the impossibility to dissociate between theory and practice in
Statistics. Applications to different areas of Science are presented using
modern tools from Time Series, Multivariate Analysis, Spatial Statistics
and Extreme Value Theory. We hope the examples will convey the message on
how a statistician could/should work. The presentation will focus on the
practical results from the analyses, but will also succintly present the
theoretical tools developed to obtain them (slides in Portuguese and talk in English).
