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Speaker "Kyle Ambert" Details Back

 

Topic

Reproducible Analytical Pipelines on the Trusted Analytics Platform

Abstract

Creating production-ready analytical pipelines can be a messy, error-prone undertaking. Kyle Ambert explores the Trusted Analytics Platform, an open source-based platform that enables data scientists to ask bigger questions of their data and carry out principled data science experiments—all while engaging in iterative, collaborative development of production solutions with application developers.

Profile

Kyle Ambert is lead data scientist at Intel’s Artificial Intelligence and Analytics Solutions group, where he uses machine learning and statistical methods to solve real-world big data problems. Currently, his research centers around novel applications of machine learning in the health and life sciences. Kyle contributes to the data science direction of the Trusted Analytics Platform, particularly as it pertains to analytical pipeline and algorithm development. He holds a BA in biological psychology from Wheaton College and a PhD in biomedical informatics from Oregon Health & Science University, where his research focused on text analytics and developing machine-learning optimization solutions for biocuration workflows in the neurosciences.