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Vasant Dhar

announcing

With automation threatening to remake much of the 21st century economy, Vasant proposes a framework for determining “When to Trust Robots with Decisions, and When Not To” in his HBR article.

Vasant was a recent guest on NPR’s Marketplace and Bloomberg TV to discuss the issues surrounding Uber’s use of self-driving cars in Pittsburgh

In his 2016 Harvard Business Review article, Vasant offers a framework for evaluating what tasks to entrust to robots.

Vasant was named Editor-in-Chief of the prestigious journal Big Data in February 2014.

Vasant was awarded a prestigious five-year research partnership with NYU-AIG in January 2014.

Vasant Dhar is a professor at the Stern School of Business and the Center for Data Science at New York University and the Editor-in-Chief of the journal Big Data. A pioneer in the field of data science, Dr. Dhar’s research focuses on a simple, yet critical, question: when do computers make better decisions than humans?

Through the use of artificial intelligence and advanced machine learning, he has explored this question and its real-world applications in settings as diverse as financial trading floors, hospital emergency rooms, preschool classrooms, and sports locker rooms. The first person to use machine learning for predictive modeling on Wall Street, Dr. Dhar’s research focuses on building scalable decision-making systems from large sources of data.

Vasant is the author of three books, including Seven Methods for Transforming Corporate data Into Business Intelligence and Intelligent Decision Support Methods: The Science of Knowledge Work and his findings have been published in the Harvard Business Review, Financial Times, Wired Magazine, and Big Data, among others.

Dr. Dhar received his Bachelors of Technology from the Indian Institute of Technology in Delhi, and his Masters of Philosophy and Doctor of Philosophy from the University of Pittsburgh.

Books:

Seven Methods for Transforming Corporate Data Into Business Intelligence

Intelligent Decision Support Methods

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Editorials:

Why Taxing High Frequency Trading Won’t Work, October 2015, CNBC

The Scope and Challenges for Deep Learning, September 2015, Big Data Journal

Should You Trust Your Money to a Robot? June 2015, Big Data Journal

Bright Lights, Big Data: What the Market May Tell Us About a Correction, November 2014, CNBC

Caught Between a Rock and a Hard Place, March 2014, Financial Times

Speed – The Only HFT Advantage? Not So Fast, April 2014, CNBC

Google in Jeopardy, October 2013, Wired

Business Schools Face a Challenging Future, January 2013, Financial Times