Gideon Mann, Head of Data Science, Bloomberg
The New Alchemy: Turning Words into Signals
News has always moved markets. Recently, machine learning methods have been able to extract meaningful structured information from text streams. This means that operations that were previously manual, like extracting fundamental data from quarterly and annual reports, can now be done automatically. Beyond this, natural language processing has also been applied to create new signals from text streams. In this talk, I'll present work done at Bloomberg on table recognition and extraction, an automated sentiment metric on equities, and an indicator for news that's likely to move the market.
Gideon Mann is the head of Data Science at Bloomberg, guiding the strategic direction for machine learning, natural language processing, and search on the core terminal. At Bloomberg, his team has worked on the company-wide data science platform, natural language question answering, and deep learning text processing, among other products. He also founded and leads the Data for Good Exchange, an annual conference on data science applications for social good and is a core member of the Shift Commission on Work, Workers and Technology (shiftcommission.work). Mann graduated Brown University in 1999 and subsequently received a Ph.D. from The Johns Hopkins University in 2006. His focus at Hopkins was natural language processing with a dissertation on multi-document fact extraction and fusion. After a short post-doc at UMass-Amherst working on problems in weakly supervised machine learning, he moved to Google Research in NYC in 2007. In addition to academic research, his team at Google built core internal machine learning libraries, and publicly released the Google Prediction API and coLaboratory, a collaborative iPython application. He joined Bloomberg's leadership team in the CTO department in 2014.