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Also Attend: 
Model Management Workshop: The magic behind making decisions happen; ML, AI, & prescriptive analytics within business design & control constraints
New York City: February 26th & 27th 2018  


Pre-Conference Workshop day: Wednesday February 28th 2018

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


Thursday March 1st 2018: Main Conference Day One Stream


Friday March 2nd 2018: Main Conference Day Two Stream


Speakers:

Keynote: O. Ediz OzkayaO. Ediz Ozkaya: (Machine Learning)

Executive Director, Machine Learning Labs, Securities,

Goldman Sachs


Keynote: Marcos Lopez de PradoMarcos Lopez de Prado

Research Fellow

Lawrence Berkeley National Laboratory


Miquel Noguer AlonsoDr. Miquel Noguer Alonso

Adjunct Assistant Professor

Columbia University


Abdel LantereAbdel Lantere

Data Scientist, Quantitative Consultant

HSBC


Michael Beal

CEO

Data Capital Management


Gordon Ritter Gordon Ritter

Senior Portfolio Manager

GSA Capital 


Jess StauthJess Stauth

Managing Director

Quantopian


Yves HilpischImage result for yves hilpisch

Founder and Managing Partner

The Python Quants 


Paul Bilokon Paul Bilokon, PhD

Founder, CEO,Thalesians

Senior Quantitative Consultant, BNP Paribas


George A. Lentzas George A. Lentzas

Manager & Chief Data ScientistSpringfield Capital

Adjunct Associate Professor, ColumbiaNew York University  


Arun Verma Arun Verma

Quantitative Research Solutions

Bloomberg, LP


Peter Hafez Image result for peter hafez ravenpack

Chief Data Scientist

RavenPack 


Suhail Shergill

Suhail Shergill

Director | Data Science and Model Innovation

Scotiabank | Global Risk Management


Seong Seog Lee

Director of Quant Strategy

Quantopian  


Joe Jevnik

Senior Software Engineer

Quantopian


 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

Conference Bookings: Discount Structure:


  • Super Early Bird Discount: 25% Until January 26th 2018
  • Early Bird Discount: 10% Until February 9th 2018
  • SPECIAL OFFER: When 2 colleagues attend the 3rd goes free!
  • Main Conference + Workshop ($250 Discount)
  • 70% Academic Discount (FULL-TIME Students Only) 

 Important notes:


Main Conference presentation files on USB memory sticks will be provided on arrival. The Main Conference files will also be made available for download via a password protected website before the event. Please print out each presentation if you wish to have hard copies before the conference and bring them with you.

Also, Wi-Fi access will be available at the venue to view presentations on laptops and mobile devices