Rensselaer data analytics students, researchers and colleagues are analyzing the data emerging from the 2019-2020 COVID-19 outbreak/pandemic. In early March 2020 The Rensselaer IDEA started gathering a set of resources on exploring and modelling current global data. This list will be updated periodically as new resources become available. Many thanks to our friends and colleagues near and for for contributing to this list!
User expectations of modern web applications continue to outpace the productivity of the software engineering process. The semantic web application stack of RDF, SPARQL, and related technologies, provides many benefits to users and developers, at the expense of additional complexity that impedes the engineering process. I will present a new software architecture that attempts to maximize the user-visible benefits of these technologies while minimizing their impact on developer productivity.
Recent years have seen a rising interest in combining the rich structured information contained in knowledge graphs with applications involving personalization. This talk will discuss insights into the emerging topic of "personal knowledge graphs", including key research challenges identified in literature and examples of proposed personal knowledge graph systems (especially in the domain of personalized health).
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.
Welcome to RPIrates: The RPI R Users Group! We are a growing community --- over 350 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 explain useful packages, share "feats of strength," and of course "air grievances!"
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.
