Sanjiv Das is the William and Janice Terry Professor of Finance at Santa Clara University's Leavey School of Business.
He previously held faculty appointments as 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, Ahmedabad, B.Com in Accounting and Economics (University of Bombay, Sydenham College), and is also a qualified Cost and Works Accountant. He is a senior editor of The Journal of Investment Management, co-editor of The Journal of Derivatives, and Associate Editor of other academic journals. Prior to being an academic, he worked in the derivatives business in the Asia-Pacific region 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 eighty articles in academic journals, and has 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.
Multimodal Machine Learning at Scale: AI for Research in Finance
Abstract
Data analytics is mostly geared towards tabular data (numerical and categorical). Humans form decisions using not only tabular data but also make judgments based on text they read, such as news, reports, etc. Econometrics and Machine learning have been used successfully on tabular data and also on text and images, but the combination of text and tabular data is much more powerful. This is especially useful in finance, where humans have been using multimodal data to make decisions, and the talk will showcase the value of this approach with new tools.