Machine Learning in Finance Workshop 2016

The workshop is organized by:

The Data Science Institute (DSI) at Columbia University and Bloomberg LP are pleased to announce a workshop on "Machine Learning in Finance".  The workshop will be held at Columbia University under the auspices of the Financial and Business Analytics Center, one of the constituent centers in the DSI, and the Center for Financial Engineering.

Date: Friday April 1, 2016
Location: 417 International Affairs Building, Columbia University, 420 W. 118th Street, New York, NY 10027. (Map)

Workshop Program

8.00 - 8.45 Registration

8.45 - 9.00 Introduction

9.00 - 9.45 Mark Dredze (Johns Hopkins University and Bloomberg LP) 
    Title: How Mining News and Social Media Inform Finance

9.45 - 10.30 
Paul Ma (Carlson School of Management, University of Minnesota) 
    Title: Strategic Misclassification

10.30 - 11.00 Coffee break

11.00 - 11.45 Sid Dalal (AIG) 
    Title: Transformation of the Insurance Industry: New Trends in Data, Analytics and Risk

11.45 - 12.30 
 Kay Giesecke (Stanford University)
    Title: Deep Learning for Mortgage Risk

12.30 - 1.30 Lunch (A boxed lunch will be provided to all attendees.) 

1.30 - 14.15 Marcos Lopez de Prado (Guggenheim Partners) 
    Title: Hierarchical Risk Parity: a Machine Learning Approach

14.15 - 15.00 Paul Glasserman (Columbia Business School) 
    Title: Does Unusual News Forecast Market Stress?

15.00 - 15.30 Coffee break

15.30 - 16.15 Raffaella Sadun (Harvard Business School)
    Title: CEO Behavior and Firm Performance

16.15- 17.00 Alexander Statnikov (American Express)  
    Title: Machine Learning at American Express
17.00 Wine and cheese reception


Online registration is available here.
Early registration is available until March 10th after which regular registration rates will apply. The early (regular) registration rates are:

Corporate delegates: $100 ($150)
Non-Columbia students*: $30 ($40)
Columbia students*: $20 ($30)
*Those availing of student rates will be required to show valid student ID at the event.
Please contact Martin Haugh <> for further details.

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