Karthik Kannan

Eller College of Management

Karthik Kannan

Eller College of Management

Karthik Kannan is Halle Chair of Leadership and the dean of Eller College of Management, University of Arizona.  He joined in 2022.  Previously, he spent 19 years at Purdue's Krannert School of Management in various capacities, including as the Associate Dean for Research and Partnerships, founding academic co-director for MS (Business Analytics and Information Management).

Professor Kannan is a thought leader in digital transformations, analysis of digital traces, and strategic foresighting.  He has published many papers in leading management journals and has won various awards.  For 2017-18, he was awarded the prestigious Jefferson Science Fellowship by the National Academies of Sciences and Engineering.

His undergraduate degree is in Electrical and Electronics Engineering from NIT Trichy (formerly REC Trichy).  Before joining Purdue, he obtained his Ph.D. in information systems, MS in Electrical and Computer Engineering, and MPhil in Public Policy and Management, all from Carnegie Mellon University.  Before joining the graduate school, he worked with Infosys Technologies.

His research interest in about 300 words: He studies problems at the intersection of technology and business.  His doctoral work focused on the role of information in determining sequential auction outcomes.  Eventually, that work spawned interests in three methodologically different directions: a) computational, b) empirical, and c) behavioral economics work, with theory continuing to be the strong foundation for much of the research.  Theoretically, he has studied the problem of pricing in data networks.  He has specifically analyzed problems in peer-to-peer content delivery networks, cardinality bundles (e.g., as they are used in data networks), and the welfare implications in security markets.  The theoretical understandings of related mechanisms were crucial for him to arrive at the innovation on congestion pricing, which was jointly patented with AT&T.  For problems intractable with theory, he has used computational methods.  Specifically, he used Q-learning-based agents to study the role of information problems that are not tractable to theoretical analysis and to analyze frictions in GSP auctions.  One of those papers was done in 2003 when Machine Learning tools were foreign to most business school research.  In 2010, he co-authored a paper that used texting mining software to demonstrate that textual features on SEC filing by firms in the stock markets were correlated with security breaches.  Most empirical work initially focused on econometrics models, such as studying the effect of incentives on retail review writing platforms.  Recently, he has become interested in investigating empirical problems with theoretical underpinnings.  One problem is creating a decision-making tool in a situation where the causal structure of the data is only vaguely known.   His team implemented this solution for a Fortune 100 company.  He has also established a new kind of strategic best response fair machine learning algorithm.  He has studied behavioral economics problems related to information revelation in auctions, piracy, etc.

 

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