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London: Machine Learning and AI in Quantitative Finance Conference, 14/15/16 March 2018

Event Details

London: Machine Learning and AI in Quantitative Finance Conference, 14/15/16 March 2018

Time: March 14, 2018 to March 16, 2018
Location: Central London
City/Town: London
Website or Map: http://quantechconference.com/
Phone: +44 (0) 1273 201 352
Event Type: conference
Organized By: Neil Fowler
Latest Activity: Dec 7

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Event Description

Pre-Conference Workshop day: Wednesday 14th March

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

  • Thursday 15th March: Main Conference Day One
  • Friday 16th March: Main Conference Day Two

Speakers:

  • O. Ediz Ozkaya: Executive Director, Machine Learning Strategist, Securities Division, Goldman Sachs
  • Daniel Giamouridis: Global Head of Scientific Implementation, Bank of America Merrill Lynch
  • Lawrence Edwards: Executive Director, Morgan Stanley
  • Daniel Drummer: Vice President, Corporate & Investment Bank FinTech, J.P. Morgan
  • Miquel Noguer Alonso: Executive Director, UBS & Adjunct Assistant Professor, Columbia University
  • Abdel Lantere, Data Scientist, Quantitative Consultant, HSBC
  • William McGhee: Global Head of Quantitative Analytics, NatWest Markets
  • Paul Bilokon: Founder, CEO,Thalesians, Senior Quantitative Consultant, BNP Paribas & Visiting Lecturer, Imperial College
  • Ignacio Ruiz: Founder & CEO, MoCaX Intelligence
  • Claudi Ruiz Camps: Machine Learning, Deep Learning Specialist, ABN AMRO Clearing Bank N.V.
  • Saeed Amen: Quant strategist & trader, Cuemacro
  • Farhan Feroz: eFX Quantitative Trader, UBS
  • Pawel Chilinski: Quantitative Trader, UBS

Topics:

  • Predictive Power vs. Expressiveness of Machine Learning Models
  • Machine Learning: History and Implications for Quantitative Finance 
  • Reliable Machine Learning 
  • Black-box Machine Learning: Improving Transparency
  • Machine Learning Models
  • Fast MVA Optimisation using Chebyshev Interpolants 
  • Machine Learning - Recent Trends and Applicability to Risk and Related Areas 
  • Machine Learning, High-Frequency Trading and Kdb+/q for Quants and Data Scientists 
  • Unsupervised Anomaly Detection in Finance
  • Practical Aspects of Applying Deep Learning for Market Making 
  • Applications & Challenges of using Deep Learning & Bayesian Inference Methods for High Frequency Market Making
  • Co-creating Machine Learning solutions within a global Corporate & Investment Bank
  • Using Big Data to Trade FX (& Python for finance)  
  • Can AI help FRTB?”
  • Time Series Data & FRTB - time to get it right

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