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NYC: Machine Learning and AI in Quantitative Finance Conference, 28 February, 1/2 March 2018

Event Details

NYC: Machine Learning and AI in Quantitative Finance Conference, 28 February, 1/2 March 2018

Time: February 28, 2018 to March 2, 2018
Location: Central London
City/Town: New York City
Website or Map: http://quantechconference.com/
Phone: +44 (0) 1273 201 352
Event Type: conference
Organized By: Neil Fowler
Latest Activity: Dec 8, 2017

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Event Description

Pre-Conference Workshop day: Wednesday February 28th

Machine Learning in Finance : A Practical View by Miquel Noguer Alonso: UBS & Columbia University

  • Thursday March 1st: Main Conference Day One
  • Friday March 2nd: Main Conference Day Two

Speakers:

  • O. Ediz Ozkaya: Executive Director, Machine Learning Strategist, Securities Division, Goldman Sachs
  • Marcos Lopez de Prado, Senior Managing Director, Guggenheim Partners
  • Daniel Giamouridis: Global Head of Scientific Implementation, Bank of America Merrill Lynch
  • Lawrence Edwards: Executive Director, Morgan Stanley
  • Suhail Shergill: Head of R&D and Innovation Lead, Scotiabank
  • Miquel Noguer Alonso: Executive Director, UBS & Adjunct Assistant Professor, Columbia University
  • Abdel Lantere, Data Scientist, Quantitative Consultant, HSBC
  • Paul Bilokon: Founder, CEO,Thalesians, Senior Quantitative Consultant, BNP Paribas & Visiting Lecturer, Imperial College
  • Gordon Ritter: Senior Portfolio Manager, GSA Capital 
  • Rajesh T. Krishnamachari: Vice President, Quantitative and Derivatives Strategy, J.P. Morgan (To be confirmed)
  • Ryan Faulkner: Officer, Federal Reserve Bank of New York
  • Arun Verma: Quantitative Research Solutions, Bloomberg, LP

Topics:

  • Predictive Power vs. Expressiveness of Machine Learning Models
  • Machine Learning - Recent Trends and Applicability to Risk and Related Areas 
  • Black-box Machine Learning: Improving Transparency
  • Machine Learning - Recent Trends and Applicability to Risk and Related Areas
  • Applying Machine Learning to Evaluate Systemic Risk and Contribution of Individual SIFIs 
  • Fast MVA Optimisation using Chebyshev Interpolants 
  • The 7 Reasons Most Machine Learning Funds Fail
  • Machine Learning, High-Frequency Trading and Kdb+/q for Quants and Data Scientists 
  • Machine Learning for Trading 
  • Big Data and AI Strategies: Machine Learning and Alternative Data Approach to Investing
  • Text Mining and Market Sentiment 
  • Machine Learning & Event Detection for Trading Energy and Metal Futures
  • Extracting embedded alpha in Stocks and Commodity underlyings using statistical arbitrage/ML techniques from News/Social data

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