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IM

Ivan Montoliu Roura

Nestlé Institute of Health Sciences S.A.
Senior Data Science Specialist
Switzerland

nestleinstitutehealthsciences.com

9:00am PDT

Machine Learning Algorithmic Deep Dive (Part 1) Campbell Rehearsal Hall

10:30am PDT

Machine Learning Algorithmic Deep Dive (Part 2) Campbell Rehearsal Hall

1:00pm PDT

Introduction to SparkR (Part 1) Econ 140

2:30pm PDT

Introduction to SparkR (Part 2) Econ 140

10:30am PDT

R in machine learning competitions McCaw Hall

10:48am PDT

Implementing R in old economy companies: From proof-of-concept to production Econ 140

11:06am PDT

Taking R to new heights for scalability and performance SIEPR 130

11:24am PDT

Distributed Computing using parallel, Distributed R, and SparkR SIEPR 130

11:42am PDT

FlashR: Enable Parallel, Scalable Data Analysis in R SIEPR 130

1:00pm PDT

Group and sparse group partial least squares approaches applied in a genomics context Barnes & McDowell & Cranston

1:18pm PDT

Fast additive quantile regression in R Barnes & McDowell & Cranston

1:36pm PDT

bigKRLS: Optimizing non-parametric regression in R Barnes & McDowell & Cranston

1:54pm PDT

Modeling Food Policy Decision Analysis with an Interactive Bayesian Network in Shiny SIEPR 120

2:12pm PDT

Wrap your model in an R package! SIEPR 120

2:30pm PDT

Coffee Break w/ Poster Session McCaw Hall

3:30pm PDT

Literate Programming McCaw Hall

4:45pm PDT

R at Microsoft McCaw Hall

5:03pm PDT

Deep Learning for R with MXNet SIEPR 130

5:21pm PDT

Interactive Naive Bayes using Shiny: Text Retrieval, Classification, Quantification SIEPR 130

5:39pm PDT

xgboost: An R package for Fast and Accurate Gradient Boosting SIEPR 130

5:57pm PDT

Visual Pruner: A Shiny app for cohort selection in observational studies Lane & Lyons & Lodato

6:30pm PDT

Welcome Reception Bing Concert Hall

9:00am PDT

Towards a grammar of interactive graphics McCaw Hall

10:30am PDT

Notebooks with R Markdown McCaw Hall

10:48am PDT

permuter: An R package for randomization inference Barnes & McDowell & Cranston

10:50am PDT

madness: multivariate automatic differentiation in R SIEPR 120

11:00am PDT

Text Mining and Sentiment Extraction in Central Bank Documents SIEPR 120

11:05am PDT

Maximum Monte Carlo likelihood estimation of conditional auto-regression models SIEPR 120

11:10am PDT

Scaling R for Business Analytics SIEPR 120

11:24am PDT

Heatmaps in R: Overview and best practices Barnes & McDowell & Cranston

11:30am PDT

Performance Above Random Expectation: A more intuitive and versatile metric for evaluating probabilistic classifiers SIEPR 120

11:42am PDT

Multivoxel Pattern Analysis of fMRI Data Lane & Lyons & Lodato

1:00pm PDT

R at Google Lane & Lyons & Lodato

1:20pm PDT

How Teradata Aster R Scales Data Science Lane & Lyons & Lodato

1:30pm PDT

Statistical Computing with R Lane & Lyons & Lodato

1:40pm PDT

Changing lives with Data Science at Microsoft Lane & Lyons & Lodato

1:54pm PDT

Spatial data in R: simple features and future perspectives SIEPR 120

2:12pm PDT

High performance climate downscaling in R SIEPR 120

3:30pm PDT

Flexible and Interpretable Regression Using Convex Penalties McCaw Hall

9:00am PDT

Statistical Thinking in a Data Science Course McCaw Hall

10:30am PDT

Gradient Boosted Trees Model: deploying R models into production environments* Lane & Lyons & Lodato

10:50am PDT

ranger: A fast implementation of random forests for high dimensional data Lane & Lyons & Lodato

11:10am PDT

Event Detection with Social Media Data Econ 140

11:25am PDT

Outlier Detection Methods Econ 140

11:40am PDT

rfmt - A new formatter for R source code Econ 140

1:00pm PDT

Catching up with Rstudio Lane & Lyons & Lodato

1:05pm PDT

Visualization of Uncertainty for Longitudinal Data SIEPR 130

1:20pm PDT

R for Big Data and Applications: Using R at Oracle Lane & Lyons & Lodato

1:35pm PDT

Scalable Machine Learning in R with H2O Lane & Lyons & Lodato

2:00pm PDT

RCloud - Collaborative Environment for Visualization and Big Data Analytics McCaw Hall

3:00pm PDT

Closing Remarks McCaw Hall