Can machines help us make better decisions? In this panel, real-world practitioners from the travel, finance, and energy industry give us an inside look at how they’re applying machine learning to their industries, oprimizing the use of resources and helping with decision support.
Jonathan has spent more than 15 years as a software developer, with a focus in the last few years on processing large data sets using tools such as Hadoop. Currently, Jonathan is a Lead Engineer on the Business Intelligence/Big Data team at Orbitz Worldwide. Jonathan is also a co-founder and organizer of the Chicago Hadoop User Group and founder of the Chicago Big Data User Group.
Rob Lancaster left a promising career in Neuroscience to work in Software Development. For the last 12 years he’s been developing solutions for the travel industry. He is currently a Solutions Architect for Orbitz working with their Hotel technology teams on various efforts.
Alasdair Allan is a scientist, author, hacker, tinkerer, and journalist who has recently been spending a lot of time thinking about the Internet of Things, which he thinks is broken. He is the author of a number of books and sometimes also stands in front of cameras. You can often find him at conferences talking about interesting things or deploying sensors to measure them. A couple of years ago, he rolled out a mesh network of five hundred sensor motes covering the entirety of Moscone West during Google I/O. He’s still recovering. A few years before that, he caused a privacy scandal by uncovering that your iPhone was recording your location all the time, which caused several class-action lawsuits and a US Senate hearing. Some years on, he still isn’t sure what to think about that.
Alasdair sporadically writes blog posts about things that interest him or, more frequently, provides commentary in 140 characters or less. He is a contributing editor for Make magazine and a contributor to O’Reilly Radar. Alasdair is a former academic. As part of his work, he built a distributed peer-to-peer network of telescopes that, acting autonomously, reactively scheduled observations of time-critical events. Notable successes included contributing to the detection of what was—at the time—the most distant object yet discovered, a gamma-ray burster at a redshift of 8.2.
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