Mathematical Finance & Financial Data Science Seminar
The mathematical finance & financial data science seminar covers a broad range of topics in mathematical and quantitative finance, including:
 Data science and machine learning in finance
 Big data and econometric techniques
 Quantitative finance
 Market microstructure and trading costs
 Portfolio and risk management
 Pricing, hedging and risk models
 Regulation and regulatory models
 Trading strategies and back testing
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This seminar series is part of the Quantitative Finance & Financial Data Science Working Group's activities at NYU Courant, organized by Petter Kolm (email: petter DOT kolm AT nyu DOT edu).
Presenters include invited visitors and NYU Courant faculty. Seminar presentations often cover original research. All seminars are held online as per the schedule below.
Seminar Organizer(s): Prof. Petter Kolm
Past Events

Tuesday, May 7, 20195:30PM, Warren Weaver Hall 1302
An Algorithmic Approach to Taxable Investing
Adam Grealish, Director of Investing, Betterment 
Tuesday, April 30, 20195:30PM, Warren Weaver Hall 1302
Reduced Order Representation of Implied Volatility Surfaces
Andrew Papanicolaou, NYU Tandon 
Friday, April 26, 20195:30PM, Warren Weaver Hall 109
Machine Learning for Trading
Gordon Ritter, Courant Institute of Mathematical Sciences 
Tuesday, April 23, 20195:30PM, Warren Weaver Hall 1302
Deep (Supervised or Otherwise) Learning
Dhruv Madeka, Senior Machine Learning Scientist, Amazon 
Tuesday, April 2, 20195:30PM, Warren Weaver Hall 1302
The Adaptive Curve Evolution Model for Interest Rates
Matthias Heymann, Goldman Sachs, Model Risk Management 
Tuesday, March 5, 20195:30PM, Warren Weaver Hall 1302
Repo Haircut and Pricing
Wujiang Lou, HSBC