Fit to read: Making your figures legible in print and on the slide
Rendering your images at high resolution is only the first step towards creating legible figures. We will discuss how to render figures so that they are readable on the page, and why sometimes using the same rendering of a figure for print and PowerPoint can leave everyone dissappointed.
RPIrates: The RPI R Users Group
Welcome to RPIrates: The RPI R Users Group! We are a growing community --- over 300 email subscribers! --- of students, faculty and staff at Rensselaer who gather virtually and in person to support each others' use of the R open source analytics platform. In semi-weekly meetings we explore topics of interest, share our "feats of strength," and of course "air grievances!"
MORTALITYMINDER
MortalityMinder (MM) is a web-based visualization tool that enables interactive exploration of social, economic and geographic factors associated with premature mortality among mid-life adults ages 25-64 across the United States. Using authoritative data from the CDC and other sources, MM is a freely available, publicly-accessible, open source, and easily maintained tool. The goal of MortalityMinder (MM) is to enable healthcare researchers, providers, payers, and policy makers to gain actionable insights into how, where, and why midlife mortality rates are rising in the United States (US). It is designed to help healthcare payers, providers and policymakers at the national, state, county and community levels identify and address unmet healthcare needs, healthcare costs, and healthcare utilization.
Introduction to Differential Privacy
This talk will be aimed at an audience unfamiliar with the literature on privacy preservation (as I was a few weeks ago). The goal of the talk will be to first illustrate that whether or not the output of some interaction with real data is privacy preserving is not as simple of a concept as it may first seem, motivating the need for a precise definition of privacy preservation. Then I will give one possible definition, that of differential privacy, invented in 2006, for which the authors were awarded the Gödel Prize in 2017.