Podcasts and Videos

Remote video URL
February 18, 2021
Data transformation and management are key to success with R, so we'll talk about Hadley Wickham's 'tidyverse', a collection of R packages that reinforce the principles of his "Tidy Tools Manifesto": * Reuse existing data structures. * Compose simple functions with the pipe operator. * Embrace functional programming. * Design for humans.
Remote video URL
RPIrates Tutorial: Rock your Dataviz World with ggplot2!
February 11, 2021

RPIrates: The RPI R Users Group and the Rensselaer Data INCITE program present this version of the famous ggplot2 tutorial, re-constructing a famous figure from The Economist step-by-step. Demonstrates current R methodology including tidyverse and many aspects of ggplot2. 

Remote video URL
December 10, 2020

Plan to join us WEDS, 09 Dec for a very, VERY special TWed as the Tetherless World Constellation holds another "virtual" version of our end-of-term Graduate Research
"Lightning Talks." TWed Lightning Talks are a great way for the TWC community and friends to learn of the wide range of amazing research happening in the Tetherless World, and "a good time is had by all!"

Lightning talks are VERY short --- approx. 2-3 minute! --- summaries by our students of current research work, with no NO SLIDES and only brief "crib notes."

Remote video URL
November 19, 2020

This talk will be an introduction to the study of privacy attacks in the context of machine learning, aimed at those unfamiliar with the literature. We will discuss who the stakeholders are, what information may be attacked, how it may be attacked, and why. The “how” will be at a high level, illustrated through some specific examples of privacy attacks. Much of the material will be from a recent survey of privacy attacks by Maria Rigaki and Sebastian Garcia at Czech Technical University in Prague, although their threat model will be extended slightly to consider cases that include synthetic data. The goal of the talk is to give the audience an appreciation of some of the complications of privacy preservation (i.e. that it’s not as simple as it may be assumed to be) and familiarity with some of the terminology.

Remote video URL
November 12, 2020

Miao will discuss the tools and techniques used to assess, visualize, and improve equity in clinical trials. A set of novel equity metrics for clinical trials is constructed from Machine Learning (ML) Fairness Research to quantify inequities of various subgroups defined over multiple demographic or clinical characteristics, such as Hispanic female subjects who are underweight or no-Hispanic black male subjects aged over 64 and with high fasting glucose level. A tool called TrialEquity, which is developed based on the proposed equity metrics, is designed to provide insights to improve the clinical trial equity and health equity, with specific considerations for diverse user groups including clinicians, researchers, and health policy advocates. The tool is able to design new equitable  clinical trials, evaluate ongoing/conducted studies, provide remedial advice for inequitable trials,  accommodate how evidence from trials applies to the individual needs of patients, and guide equitable decisions for users.

Remote video URL
MortalityMinder: A Web Tool for Visualizing and Investigating Social Determinants of Premature Mortality in the United States
November 10, 2020

Challenge: Midlife mortality rates are rising in the United States (US), while in many other nations, mortality rates are decreasing. For example, Stein et al. (2017) found that “Deaths of Despair” due to suicide and substance abuse have increased dramatically among white males between the ages of 25-64 particularly in rural America. The MortalityMinder (MM) app’s goal 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 US.System

Description and Purpose: Using county-level data on mortality rates from CDC WONDER, MM explores mortality trends for adults ages 25-64 in the US from 2000 to 2017. Using county-level surveillance data from County Health Rankings, MM identifies social and economic factors associated with mortality trends at the county level for the US and individual states. The user selects the region (specific state or US) and the cause of death (All Causes,Cancer, Cardiovascular, or Deaths of Despair). MM divides counties into mortality risk groups using clustering and then finds statistical associations between groups and putative risk factors. MM dynamically creates three analysis and visualization infographics, each addressing a different question: