Eric Siegel, PhD—This book's author

• Founder of the Predictive Analytics World conference series.
• Executive editor of The Predictive Analytics Times.
• Instructor of the online, on-demand training workshop Predictive Analytics Applied (www.businessprediction.com).
• Former computer science professor at Columbia University.
• For more information, see About the Author.

John Elder, PhD

Invested his entire life savings into his own predictive stock market trading system (see Chapter 1).

• CEO and founder, Elder Research, Inc.
• Founding conference chair of Predictive Analytics World for Government, and a frequent keynote speaker at other Predictive Analytics World events.
• Coauthor, Handbook of Statistical Analysis and Data Mining Applications.
• Adjunct professor at the University of Virginia.

Gitali Halder and Anindya Dey

Led a staff-retention project that earmarks each of Hewlett-Packard's almost 350,000 worldwide employees according to “Flight Risk,” the expected chance they will quit their jobs (see Chapter 2).

• Analytics practitioners at Hewlett-Packard.
• Backgrounds in statistics and economics.

Andrew Pole

Led a marketing project at Target that predicts customer pregnancy (see Chapter 2).

• Analytics manager at Target.
• Previously a lead consumer analyst at Hallmark Cards.
• Master's degrees in statistics and economics.
• View his original newsbreaking presentation on pregnancy prediction: www.pawcon.com/Target.

Dan Steinberg, PhD

Led the prediction of outcome for millions of mortgages at Chase Bank (see Chapter 4).

• President and founder, Salford Systems.
• Entrepreneur who delivers state-of-the-art predictive modeling from the research lab to commercial deployment.
• PhD in economics from Harvard University.
• Former University of California professor.

Martin Chabbert and Martin Piotte

With no background in analytics, they took Netflix's $1 million predictive contest by storm (see Chapter 5).

• Software engineers in the telecommunications industry.

David Gondek, PhD

Led the design of machine learning integration for IBM's Jeopardy!-playing computer, Watson (see Chapter 6).

• Research scientist at IBM Research.
• PhD in computer science from Brown University.

Roger Craig, PhD

Prepared to compete on the TV quiz show Jeopardy! by developing a model that predicted which practice questions he'd get wrong in order to target his many hours of studying (see Chapter 6).

• Attained the highest ever Jeopardy! one-day winning total and won the show's 2011 Tournament of Champions.
• Over 10 years of experience applying predictive analytics across multiple industries.

Eva Helle

At a large telecom, Telenor, she predictively optimized how best to persuade each cell phone customer to stay (see Chapter 7).

• Customer analytics lead at Europe's Telenor, the world's seventh-largest mobile operator.
• Master's degree in statistics and marketing.

Rayid Ghani and Daniel Porter

Helped Barack Obama's 2012 presidential campaign employ persuasion modeling in order to predict which individual voters would be positively influenced by campaign contact (a call, door knock, flier, or TV ad), and which would be driven to vote adversely by contact (See Chapter 7's sidebar).

• Chief data scientist and director of statistical modeling, respectively, Obama for America 2012 Campaign.
• Experts in persuasion modeling, aka uplift modeling.