Why R? 2020 Keynotes

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Why R 2020 Keynotes sessions are close! Check out below posts to find out dates and descriptions of keynote talks for 2020.whyr.pl.

Why R? 2020 Keynotes

Julia Silge – Wednesday 5pm UTC (2020-09-23)

Julia Silge is a data scientist and software engineer at RStudio PBC where she works on open source modeling tools. She is an author, an international keynote speaker, and a real-world practitioner focusing on data analysis and machine learning practice. Julia loves text analysis, making beautiful charts, and communicating about technical topics with diverse audiences.

Visual representations of data inform how machine learning practitioners think, understand, and decide. Before charts are ever used for outward communication about a ML system, they are used by the system designers and operators themselves as a tool to make better modeling choices. Practitioners use visualization, from very familiar statistical graphics to creative and less standard plots, at the points of most important human decisions when other ways to validate those decisions can be difficult. Visualization approaches are used to understand both the data that serves as input for machine learning and the models that practitioners create. In this talk, learn about the process of building a ML model in the real world, how and when practitioners use visualization to make more effective choices, and considerations for ML visualization tooling.

Riinu Pius – Saturday 9am UTC (2020-09-26)

R for Health Data Science: from clinicians who code to Shiny interventions

Riinu Pius is a data scientist at the Centre for Medical Informatics, The University of Edinburgh. She’s the author of the brand new “R for Health Data Science” book written with Ewen Harrison.
In her talk she will walk you through cool and innovative ways R can be used to facilitate health research, from clinical trials to global cohort studies. The talk includes practical tips and code examples.

Frank Harrell – Saturday 1:45pm UTC (2020-09-26)

Frank is a professor and a founding chair of the Department of Biostatistics at Vanderbilt University School of Medicine. Aside from more than 300 scientific publications, Frank has authored Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (2nd Edition 2015, Springer-Verlag), which still serves as a primer in modern statistical modeling for generations of statisticians. His specialties are development of accurate prognostic and diagnostic models, model validation, clinical trials, observational clinical research, cardiovascular research, technology evaluation, pharmaceutical safety, Bayesian methods, quantifying predictive accuracy, missing data imputation, and statistical graphics and reporting.

Jan Vitek – Saturday 3:30pm UTC (2020-09-26)

R MELTS BRAINS – or: How I Learned to Love Failing at Compiling R

Jan Vitek is a Professor of Computer Science at Northeastern University. He holds degrees from the University of Geneva andd Victoria. He works on topics related to the design and implementation of programming languages. In the Ovm project, he led the implementation of the first real-time Java virtual machine to be successfully flight-tested. Together with Noble and Potter, he proposed a concept that became known as Ownership Types. He was one of the designers of the Thorn language. He worked on gaining a better understanding of the JavaScript language and is looking at how to support scalable data analysis in R. Prof. Vitek chaired ACM SIGPLAN; he was the Chief Scientist at Fiji Systems and the founding team at H2O.ai, a vice chair of AITO; a vice chair of IFIP WG 2.4, and chaired SPLASH, PLDI, ECOOP, ISMM and LCTES and was program chair of ESOP, ECOOP, VEE, Coordination, and TOOLS.

Wouldn’t you like to have you R code run as fast as C? What if you could write a loop without fear of waiting for hours that it complete? Wouldn’t it be nice if you could load massive datasets and be confident that R can handle them? Wouldn’t it be cool if R was the next Julia? This talk is about how to compile R programs into native executables. Replacing R’s interpreter with a compiler and thus getting code that is both fast and efficient in memory. It tells the story of years of attempts and explains why fast R is still not there yet. We will touch on different ways to reach the goals and how those approach would affect the user experience. The story is not pretty and then ending is still uncertain. Enter at your own perils.

Roger Bivand – Sunday 9am UTC (2020-09-27)

Applied Spatial Data Analysis with R: retrospect and prospect

Roger Bivand, an active R user and contributor since 1997, is a professor at Norwegian School of Economics. Roger has contributed to and led the development of several of the core R packages for spatial analysis, including rgdal, sp, sf and maptools. His contributions helped in advancing the status of R as the tool for spatial statistics. His involvement in the open software community is exemplified by his participation in the work of R Foundation, and as editor of the R Journal 2015-2018. Roger’s passion for spatial analysis resulted not only in numerous scientific publications, but also in the authorship of the Applied Spatial Data Analysis with R book (https://asdar-book.org/
) and the winner of the OpenGeoHub Life Achievement award.

When we began over 20 years ago, spatial data was usually found in proprietary software, usually geographical information systems, and most positional data was very hard to acquire. Statistics for spatial data existed, but largely without convenient access to positional data. Using S-Plus with ArcView (or GRASS) was popular, but costly; for teaching and field research, R and open source geospatial applications and software libraries provided a feasible alternative. Starting from writing classes for spatial data in R in 2003, we first used the classes in our own
analysis packages; our book first appeared in 2008. At that time, a handful of packages used these classes, but now the R spatial cluster of CRAN packages using spatial classes is almost 900 strong. This places a burden of responsibility on us, to juggle the needs of these packages against the advances in crucial geospatial libraries and changes in industry standards for representing positional data. The early insights into why statistics for spatial (and spatio-temporal) data are challenging remain equally valid today; more data are available, but spatial patterning and scale remain interesting problems.

Małgorzata Bogdan – Sunday 1:45 UTC (2020-09-27)

Recent developments on Sorted L-One Penalized Estimation

Sorted L-One Penalized Estimator is an extension of LASSO, which allows for a reduction of dimension by eliminating some of the model parameters as well as by making some of them equal to each other. In this talk we will present some of the recent developments on SLOPE, with the specific emphasis on the Adaptive Bayesian version of SLOPE and on the strong screening rule, which allows a substantial speeding up of the SLOPE algorithm.

Małgorzata Bodgan is an associate professor of statistics at University of Wrocław. She focuses on statistical methods for filtering and modeling high-dimension data. She conducted her research at University of Washington, Purdue University, University of Vienna, Lund University and Stanford University. Małgorzata has published over fifty scientific publications and her achievements earned her a “Women for Math Science Award” from the Department of Mathematics of Munich University of Technology and Fullbright Scholarship

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