Jake Porway: Data without Borders
What happens when ambitious and talented data scientists are connected with social organisations rife with data but lacking resources to do anything with it? Jake Porway’s ‘Data Without Borders’ helps bring these two groups together, using data in the service of humanity to design transformative visualisations and decision-making tools.
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ABOUT Jake Porway
“Jake Porway has a love of all things related to pattern recognition, data visualisation, and that happy union when a huge amount of information slams into something smart enough to do something with it. He revels in the divide between academic expertise and industrial know-how and is just as excited to pioneer the next machine learning algorithm as he is to optimise the code for it.
Jake is also an abiding fan of art and socially responsible living, loves learning new languages and musical instruments, and lives with a very fat cat. He joined the New York Times Research & Development group in December 2010, where he is currently exploring the relationships between social media and the process of creating and sharing news, developing interfaces for interaction with new devices, and otherwise redefining media as we know it in his role as Data Scientist.
He holds a B.S. in Computer Science and a Ph.D. in Statistics from UCLA, where his research focused on learning hierarchical and contextual grammar models for patterns in images, including recognising object categories as well as modeling aerial images.
Jake was previously a Research Scientist at UtopiaCompression, where he lead and worked on a load of fun and exciting machine learning and computer vision projects, including active and incremental learning, anomaly detection, high-dimensional data visualisation and abstraction, and image-to-text conversion. Through internships and collaborations, Jake has also had the privilege of working at Google and Bell Laboratories, as well as contracting projects with the Office of Naval Research, DARPA, and NASA’s Jet Propulsion Laboratory.”
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