Exploring Dark Energy & Star Formation w/ ML with Viviana Acquaviva

800 800 This Week in Machine Learning & AI

In today’s episode of our Strata Data series, we’re joined by Viviana Acquaviva, Associate Professor at City Tech, the New York City College of Technology.

Viviana led a tutorial at the conference, titled “Learning Machine Learning using Astronomy data sets.” In our conversation, we begin by discussing an ongoing project she’s a part of called the “Hobby-Eberly Telescope Dark Energy eXperiment,” or HETDEX. In this project, Viviana tackles the challenge of understanding of how and why the expansion of the universe is accelerating, which is directly contrary to the principles of gravity. We discuss her motivation for undertaking this project, how she gets her data, the models she uses, and how she evaluates their performance.

Thanks to our Sponsors!

Thanks to Cloudera and Capital One for their continued support of the podcast and their sponsorship of this series.

Cloudera’s modern platform for machine learning and analytics, optimized for the cloud, lets you build and deploy AI solutions at scale, efficiently and securely, anywhere you want. In addition, Cloudera Fast Forward Lab’s expert guidance helps you realize your AI future, faster. To learn more, visit Cloudera’s Machine Learning resource center at cloudera.com/ml.

At the NIPS Conference in Montreal this December, researchers from Capital One will be co-hosting a workshop focused on Challenges and Opportunities for AI in Financial Services and the Impact of Fairness, Explainability, Accuracy, and Privacy. A call for papers is open now through October 25, for more information or submissions, visit twimlai.com/c1nips. A limited number of full NIPS Conference tickets are also available for accepted speakers (one full-price ticket only, applied directly to the accepted speaker),and will be made available along with author notifications on Oct 29, 2018.

About Viviana

Mentioned in the Interview

“More On That Later” by Lee Rosevere licensed under CC By 4.0

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