Vasant Dhar

Vasant Dhar is an Artificial Intelligence researcher and data scientist, and host of the podcast, "Brave New World," which explores how technology and virtualization in the post-COVID era is transforming humanity.

Dhar’s research focuses on the role of Artificial Intelligence in our lives. One part is about machine-based decision making and the trustworthiness of machines. The other part is about the broader impacts of data and technology in our lives, and the creation of policies for a better future in a world of intelligent machines.

Dhar’s high level framework breaks down trust into 2 risk-based dimensions. The first is inherent predictability, or how frequently a system makes mistakes. Think of this on the X-axis between zero and one. The second dimension, along the Y-axis is the cost of error of mistakes. The research demonstrates the existence of an “automation frontier” that expresses a tradeoff between how often a system will be wrong and its consequences. Trust, and hence our willingness to cede control of decision making to the machine, increases with increasing predictability and lower error costs. In other words, we are willing to trust machines if they don’t make too many mistakes and their costs are tolerable.

The automation frontier provides a natural way to think about the future of work and the division of responsibility between humans and machines.

Dhar’s research also focuses on issues of data governance and policy. He writes regularly in the media on Artificial Intelligence, societal risks of AI platforms, data governance, privacy, ethics, and trust. He is a frequent speaker in academic as well as industrial forums.

Professor Dhar teaches courses on Systematic Investing, Prediction, Data Science, and Foundations of FinTech.

He has written over 100 research articles, funded by grants from industry and government agencies such as the National Science Foundation.

Professor Dhar received his Bachelor of Technology from the Indian Institute of Technology in Delhi, and his Master of Philosophy and Doctor of Philosophy from the University of Pittsburgh.

Research Interests

• Data Science
• Predictive Analytics
• Artificial Intelligence
• Machine Learning
• Data Governance

Courses Taught

• Foundations of FinTech
• Introduction to Data Science
• Robo Advisors and Systematic Investing

Academic Background

Ph.D., Artificial Intelligence, 1984 University of Pittsburgh

M.Phil., 1982 University of Pittsburgh

B.Tech., Chemical Engineering, 1978 Indian Institute of Technology, Delhi

Awards & Appointments

• Editor-in-Chief Big Data 2014
• Principal Investigator Mining Online Delegate Data for 2012 US Presidential Elections, July 2011 2011
• Co-Principal Investigator Interdisciplinary Studies in Security and Privacy, NYU Abu Dhabi 2011
• Principal Investigator Risk-Driven Catastrophes in Global 1000 Companies, funded by Deloitte & Touche 2010
• Principal Investigator News as a Risk Factor in Predictive Modeling, supported by Thomson Reuters ('10-present) 2010
• Daniel P. Paduano Fellow ('09-'11) NYU Stern 2009
• Co-Director Center for Business Analytics 2006