Now is surely an exciting time for the media. New developments in machine learning an AI have opened new product directions. The industry has shifted toward more distributed models for content distribution. There has been a growth of content formats. There have been big changes in ad distribution, a major revenue stream, and their surrounding technologies. There are even more changes on the horizon with new european data regulation, the growth of conversational AI, and the constant improvements in cloud technologies and machine learning services.
For our opening keynote at DataEngConf NYC, please join us for a lively conversation with data experts from the media and advertising industries about the future of data science in the media.
My technical background is in theoretical condensed matter physics, particularly in the parameterization and exact solution of integrable matrices. Using this background in advanced matrix techniques I lead all of SocialFlow's research efforts. The company has unique access to Facebook and all of the Twitter firehose. The big data load is enormous and I've designed a variety of techniques for its analysis and processing. I'm eager to continue this type of work, i.e. in a company with access to large amounts of data from which we can draw concrete behavioral outcomes.
Jonathan is Head of Data Science
Chris Wiggins is an applied mathematician with a Ph.D. in theoretical physics working on computational biology. Focus areas include applications of machine learning, statistical inference, and information theory for the inference, analysis, and organization of biological networks.Along with bit.ly's Hilary Mason, he organized the HackNY Hackathon and Summer Fellows program.
Claudia Perlich serves as Chief Scientist at m6d and in this role designs, develops, analyzes and optimizes the machine learning that drives digital advertising to prospective customers of brands.An active industry speaker and frequent contributor to industry publications, Claudia enjoys serving as a guide in world of data, and was recently named winner of the Advertising Research Foundation’s (ARF) Grand Innovation Award and was selected as member of the Crain’s NY annual 40 Under 40 list.
Shawndra Hill is a Senior Researcher in the Computational Social Science Group at Microsoft Research NYC. Prior to joining Microsoft, she was on the faculty of the Operations and Information Management Department at the Wharton School of the University of Pennsylvania, where she was an Annenberg Public Policy Center Distinguished Research Fellow, a Wharton Customer Analytics Initiative Senior Fellow, and a core member of the Penn Social Media and Health Innovation Lab and the Warren Center for Network and Data Sciences. Generally, she studies data mining, machine learning and statistical relational learning and their alignment with business problems. Specifically, she researches the value to companies of mining data on how consumers interact with each other on online platforms — for targeted marketing, advertising, health and fraud detection purposes.