Much of the data created today can be described as sketchy. Far from the pristine, curated databases from yesteryear, today’s interesting datasets are incomplete, error-prone, and polluted with fraud. Data scientists are increasingly detectives and artists, investigating the (mis)behavior of raw data and painting iteratively clearer sketches as they refine raw data into usable form. Meanwhile, as datasets are sourced and coalesced from disparate producers, we need to navigate thorny issues such as privacy and data ownership. This diverse group of panelists, spanning CEOs to CIA informants, will discuss the challenges and opportunities that unfold as we wrestle with sketchy data.
Dr. Andreas Weigend studies the ongoing revolution in social data and its impact on consumers, business, and society. He teaches at Stanford University and directs the Social Data Lab. Previously, Weigend was the chief scientist of Amazon.com, where he focused on building the customer-centric and measurement-focused culture that has been central to Amazon's success.
Weigend works with innovative startups and global companies alike, helping them understand and leverage the irreversible changes in how consumers express themselves, make purchasing and lifestyle decisions, and relate to each other. His goal is to guide his clients through the evolving landscape of consumer behavior and unprecedented data to identify new business opportunities.
Weigend studied electrical engineering, physics, and philosophy in Germany and Cambridge (UK), and received his Ph.D. in physics from Stanford University. His career as a data scientist and his deep industry and startup experience allow him to successfully bridge the gap between academia and industry. He lives in San Francisco, Shanghai, and on weigend.com.
David has been a professional athlete, a union organizer, an informant for the CIA, and a founder and/or CEO of large companies and small. After serving as CEO of Thomson Financial, he has dedicated the past 13 years to venture capital and private equity, and serves on the board of Paychex. David has also previously held executive or board positions at A.C. Nielsen, IMS Healthcare, Trip Advisor, and Cognizant. He has been named to 40 under 40, the Brown University Sports Hall of Fame, and to Corporate Board Member's 10 Directors To Watch.
George has been analyzing data for customers for almost 20 years, starting with consulting jobs during his Ph.D. work at Stanford and continuing today at Rocket Fuel, where he is Chairman, CEO, and founder. Rocket Fuel was started in 2008 and has since grown to almost 400 employees globally. Recently named #4 in Forbes’ Most Promising Companies in America, Rocket Fuel is trying to solve the scientific side of marketing, with big data and AI autonomously managing and optimizing digital campaigns for hundreds of brands, evaluating over 28 billion opportunities per day, and serving 7 billion ads a month using a planet-scale computing platform. Besides Rocket Fuel, George has led hypergrowth initiatives and teams at Yahoo! and IBM and was an early pre-IPO employee at Epiphany and salesforce.com. His Ph.D. was supported by a National Science Foundation fellowship.
Claudia 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 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. She has published numerous scientific articles, holds multiple patents in machine learning, and has won many data-mining competitions. Prior to joining m6d in February 2010, Claudia worked at IBM’s Watson Research Center, concentrating on data analytics and machine learning for complex real-world domains and applications. Claudia has a Ph.D. in information systems from NYU and an M.A. in computer science from Colorado University. Claudia takes active interest in the making of the next generation of data scientists and is teaching “Data Mining for Business Intelligence” in the NYU Stern MBA program.