Sanjiv Das is the William and Janice Terry Professor of Finance at Santa Clara University’s Leavey School of Business. He was previously Associate Professor at Harvard Business School and UC Berkeley. He holds post-graduate degrees in Finance (M.Phil and Ph.D. from New York University), Computer Science (M.S. from UC Berkeley), an MBA from the Indian Institute of Management, and is a qualified Cost and Works Accountant. He edits several academic journals. Prior to being an academic, he worked in the derivatives business as a Vice-President at Citibank. His current research interests include: the modeling of default risk, machine learning, social networks, derivatives pricing models, portfolio theory, and venture capital. He has published over ninety articles in academic journals, and won numerous awards for research and teaching. His recent book “Derivatives: Principles and Practice” was published in May 2010. He currently also serves as a Senior Fellow at the FDIC Center for Financial Research.

Keynote: Text Mining and Networks for Systemic Risk Measurement
Information is extracted from big textual data to create a single risk score for the financial system. To do this network analysis is overlaid with mathematical theory to create a systemic risk monitoring dashboard. I will discuss implementations in the US and India. Extensions to stochastic networks will also be presented.

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