Applied Data Science Invited Speakers

The Applied Data Science Invited Talks will provide a venue for leading experts in the world of applied data mining and knowledge discovery. These invited talks will feature highly influential speakers who have directly contributed to successful data mining applications in their respective fields. The talks and discussions will focus on innovative and leading-edge, large-scale industry or government applications of data mining in areas such as finance, health-care, bio-informatics, public policy, infrastructure, telecommunications, social media and computational advertising.

Keynote: David Heckerman

David Heckerman


Exploiting High Dimensionality in Big Data

There are two aspects of data that make them big: sample size and dimensionality. The advantages of large sample size have long been touted. In contrast, high dimensionality has typically been seen as an obstacle to successful analysis. In this talk, using the area of genomics as an example, I will illustrate some of the advantages of high dimensionality.

David Heckerman is a Distinguished Scientist and VP at Amazon. In his current scientific work, he is developing machine-learning approaches for biological and medical applications. In his early work, he demonstrated the importance of probability theory in Artificial Intelligence, and developed methods to learn graphical models from data including methods for causal discovery. While at Microsoft, he developed numerous applications including the junk-mail filters in Outlook, Exchange, and Hotmail, machine-learning tools in SQL Server and Commerce Server, handwriting recognition in the Tablet PC, text mining software in Sharepoint Portal Server, troubleshooters in Windows, and the Answer Wizard in Office. David received his Ph.D. (1990) and M.D. (1992) from Stanford University, and is an ACM, AAAI, and ACMI Fellow.

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