Using Machine Learning to Increase Health Insurance Coverage
Ricky (@rickyhennessy) works as a Sr. Data Designer at Fjord, Design and Innovation from Accenture. Previously, he was at frog design, where he worked as a Sr. Data Scientist. Working at the intersection of data science and design, he’s been able to apply a more human centered approach to data science. He has also earned a PhD in biomedical engineering at UT Austin.
Working with a state run healthcare exchange, Ricky & team utilized their existing data to develop a machine learning model that could predict whether or not an individual was going to sign up for insurance through the exchange in the next open enrollment enrollment period. This model can then be used to inform outreach campaigns targeted at individuals at risk of dropping out of the exchange.
In Ricky’s talk, he’ll cover the entire process from defining the problem to deploying the model, and discuss how this work has led to other application of machine learning inside of CoveredCA.
A Machine Learning Win at GitHub …and So Can You!
Clair J. Sullivan (@cjisalock) is currently a data scientist on the Machine Learning team at GitHub. Prior to joining GitHub, she was a professor in Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign where she worked on the intersection of the Internet of Things and machine learning with nuclear security. She also has worked on these topics from within the federal government and the national laboratory system. In addition to her work in nuclear security, she founded La Neige Analytics to provide data science services to the ski industry. She received her PhD in nuclear engineering from the University of Michigan and has published extensively in the subjects of geospatially-enabled machine learning and nuclear security.
In October of 2017, GitHub launched a new feature called Repository Recommendations as a way to connect people to code. Users are presented with a personalized list of repositories, sorted by which ones might be of most interest to the user. Repository Recommendations are a tool to grow the open source community by connecting users to projects they might be interested in contributing to or to other users with whom they might collaborate or learn from.
Machine learning practitioners will recognize this as a type of recommendation engine, but how was this actually implemented at GitHub? This talk will present the details for how the recommendation engine was created at GitHub, the math behind the scoring, and the technology used to make it happen. In addition to talking about how this was done at GitHub, the talk will also describe how users can make their own repository recommendation engine using GitHub’s publicly-available data.
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