The final webinar of the University of Cincinnati's Analytics Summit 2021 will be held on Tuesday, June 8 from 12:00 – 2:30 PM EDT.
This session features 3 speakers
Registration is complimentary and open to all. Click here to register for this event. More information on the talks and speakers is below.
Cynthia RudinProfessor of Computer ScienceDuke University
Why use Interpretable Machine Learning? Because Predicting Manhole Fires and Brain Seizures is More Difficult Without It
Abstract: With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice. Explanations for black box models are not reliable, and can be misleading. If we use interpretable machine learning models, they come with their own explanations, which are faithful to what the model actually computes. In this talk, I will introduce interpretable machine learning, and discuss several important applications to energy grid reliability, healthcare, and criminal justice.
Bio: Cynthia Rudin is a professor of computer science, electrical and computer engineering, and statistical science at Duke University, and directs the Prediction Analysis Lab, whose main focus is in interpretable machine learning. She is also an associate director of the Statistical and Applied Mathematical Sciences Institute (SAMSI). Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD from Princeton University. She is a three-time winner of the INFORMS Innovative Applications in Analytics Award, was named as one of the "Top 40 Under 40" by Poets and Quants in 2015, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. She is past chair of both the INFORMS Data Mining Section and the Statistical Learning and Data Science section of the American Statistical Association. She has also served on committees for DARPA, the National Institute of Justice, and AAAI. She has served on three committees for the National Academies of Sciences, Engineering and Medicine, including the Committee on Applied and Theoretical Statistics, the Committee on Law and Justice, and the Committee on Analytic Research Foundations for the Next-Generation Electric Grid. She is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics. She is a Thomas Langford Lecturer at Duke University during the 2019-2020 academic year.
Alexander AntonySr. Data ScientistGE Aviation
Forecasting the Aviation Market Recovery
Abstract: COVID-19 has had a significant impact on many industries, but few have been as heavily disrupted as commercial aviation. When combined with government travel restrictions, the spread of the disease has led to a historic decline in air travel around the world. This talk will discuss how advanced forecasting methods, such as Bayesian Structural Time Series models, can be leveraged to shed light on the aviation market recovery in the aftermath of COVID-19.
Bio: Alex Antony is a Senior Data Scientist at GE Aviation in Cincinnati, OH. Prior to joining GE, Alex worked as a data scientist on Wright-Patterson Air Force Base and as a statistical consultant. Alex holds both a MS in Applied Statistics and a PhD in Political Science with a focus on Quantitative Methodology from Indiana University.
Brad BoehmkeDirector of Data Science84.51°
Scaling Productivity with an Inner Source Ecosystem
Abstract: The open source ecosystem provides many resources that most organizations leverage and benefit from. The beauty of this ecosystem is that many packages and tools exist to make you more effective and efficient; plus, you have the opportunity to contribute back to the source code. At 84.51° we have started to create our own inner source ecosystem of internal packages and tools to help make our 250+ data scientists more productive. This talk will discuss how we did it, the benefits and challenges, along with providing tips that you can take back to your organization to start building similar capabilities.
Bio: Brad Boehmke, PhD, is the Director of Data Science at 84.51°, Professor at three universities, author of the Data Wrangling and Hands-On Machine Learning with R books, and creator of multiple R open source packages and data science short courses. Brad's team focuses on developing algorithmic processes, solutions, and tools that enable 84.51° and its analysts to efficiently extract insights from data and provide solution alternatives to decision-makers. He has a wide analytic skill set covering descriptive, predictive, and prescriptive analytic capabilities applied across multiple domains including retail, healthcare, cyber intelligence, finance, Department of Defense, and aerospace. Summary of his works is available online at bradleyboehmke.github.io.
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