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A Talk by Ivailo Dimov: Some Data Science Lessons From a Quant

  • 26 Sep 2017
  • 6:00 PM (EDT)
  • NYU Kimmel Center, Room 802, 60 Washington Square South, New York, NY

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Some Data Science Lessons from a Quant

A Talk by Ivailo Dimov 

Quant Research, Bloomberg LP

Tuesday, September 26th

5:45 PM Registration
6:00 PM Presentation Begins
7:30 PM Reception

    
Abstract
With the emergence of new tools and technologies in the past few years which make large scale machine learning and analytics accessible at low cost, there is greater demand than ever for quants to be versed in data science. However, even though the flavor of data science and skills relevant in finance is somewhat different than that in the tech industry, little has been done to fill the education gap between the two flavors. In this talk we overview some of the tools, methods and recent trends in data science that are relevant to quants. We also walk through some case studies of success and pitfalls when applying data science models and tools to finance.


 

Biography

Ivailo Dimov is a member of the Quant Research team at Bloomberg LP since 2011, where he provides quant, data science and analytics solutions to senior management, external and internal clients. He is also an Adjunct Professor at the NYU Courant Institute of Mathematical Finance. At Bloomberg LP, Ivailo has worked extensively both on quantitative finance projects in equity, Tradebook, FX, credit and bonds, as well as on alternative data projects in News & Twitter, collaborative filtering, Bloomberg Sports and election modeling. Prior to joining Bloomberg, Ivailo was a quant at the Derivative Analysis group at Goldman Sachs. He holds a Physics Ph.D. and is a graduate of the Mathematics in Finance Master program at NYU Courant. Currently, he is teaching the Data Science in Quantitative Finance course with Petter Kolm at NYU.




Registration Fees:
Complimentary for IAQF members through this site
Non-Members: $25.00 by registering through this site