Tuesday, September 29
Location: 1 E10 /1 E11
9:00am - 5:00pm
Data science is a hot topic, but much of it is simply Business Intelligence in a new mantle. In this track, we push the envelope of data science, exploring emerging topics and new areas of study made possible by vast troves of raw data and cutting-edge architectures for analyzing and exploring information. We’ll cover topics such as data management, machine learning, natural language processing, crowdsourcing and algorithm design.
Who should attend: Data data scientists, data engineers, statisticians, data modellers, and analysts with a strong understanding of data science fundamentals, will find themselves at home in this tutorial, as will CTOs, Chief Scientists, and academic researchers.
Ben Lorica is the Chief Data Scientist and Director of Content Strategy for Data at O'Reilly Media, Inc. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, and financial services.
Ben Recht is an Associate Professor in the Department of Electrical Engineering and Computer Sciences and the Department of Statistics at the University of California, Berkeley. Ben’s research focuses on scalable computational tools for large-scale data analysis, statistical signal processing, and machine learning. He explores the intersections of convex optimization, mathematical statistics, and randomized algorithms. He is particularly interested in simplifying the analysis and manipulation of noisy and incomplete data by exploiting domain-specific knowledge and prior information about structure. Ben is the recipient of an NSF Career Award, an Alfred P. Sloan Research Fellowship, and the 2012 SIAM/MOS Lagrange Prize in Continuous Optimization. He is currently on the Editorial Boards of Mathematical Programming and the Journal for Machine Learning Research.
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