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 08:00: Registration and Morning Welcome Coffee


Paul Bilokon: Founder, CEO,Thalesians, Senior Quantitative Consultant, BNP Paribas   

09.00 - 09.45: Keynote: Machine Learning Enhanced Trading

Presenter: Georgios Papaioannou: 
Trading Strategist, Bank of America Merrill Lynch

09.45 - 10.30: Machine Learning & AI in Quantitative Finance Panel:


  • Paul Bilokon: Founder, CEO,Thalesians, Senior Quantitative ConsultantBNP Paribas 


  • Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University
  • Georgios Papaioannou: Trading Strategist, Bank of America Merrill Lynch
  • Abdel Lantere: Data Scientist, Quantitative Consultant, HSBC 
  • Alexei Kondratyev: Managing Director, Head of Prime Services Analytics, Standard Chartered Bank
  • Christoph Burgard: Head of Risk Analytics For Global Markets, Bank of America Merrill Lynch


  • What is the current state of utilisation of machine learning in finance?
  • What are the distinct features of machine learning problems in finance compared to other industries?
  • What are the best practices to overcome these difficulties?
  • What's the evolution of a team using machine learning in terms of day to day operations?
  • What is a typical front office 'Quant' skillset going to look like in three to five years time?
  • How do we deal with model risk in machine learning case?
  • How is machine learning expected to be regulated?
  • What applications can you list among its successes?
  • How much value is it adding over and above the “classical” techniques such as linear regression, convex optimisation, etc.?
  • Do you see high-performance computing (HPC) as a major enabler of machine learning?
  • What advances in HPC have caused the most progress?
  • What do you see as the most important machine learning techniques for the future?
  • What are the main pitfalls of using Machine Learning currently in trading strategies?
  • What new insights can Machine Learning offer into the analysis of financial time series?
  • Discuss the potential of Deep Learning in algorithmic trading?
  • Do you think machine learning and HPC will transform finance 5-10 years from now?
  • If so, how do you envisage this transformation?
  • Can you anticipate any pitfalls that we should watch out for.
  • Discuss quantum computing in quant finance:
    • Breakthroughs
    • Applications
    • Future uses

10.30 – 11.00: Morning Break and Networking Opportunities

11.00 - 11.45: Machine Learning Models   

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Deep learning
  • Advanced machine learning models    

Presenter: Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University 

11.45 - 12.30: "Black-box Machine Learning: Improving Transparency" 

"Many of the state of the art machine learning applications are based on black-box models which are difficult to interpret and explain. With more ML-based models being integrated into live decision-making systems, new challenges will be faced by various functions within banks as well as by the regulators. This talk disucsses the challenges faced and presents techniques to help provide more transparency and better understanding of the results of a given ML black-box model." 

Presenter: Abdel Lantere: Data Scientist, Quantitative Consultant, HSBC  

12.30 - 13.45: Lunch

13.45 - 14.30: From Artificial Intelligence to Machine Learning, from Logic to Probability  

Applications of Artificial Intelligence (AI) and Machine Learning (ML) are rapidly gaining steam in quantitative finace. These terms are often used interchangeably. However, the pioneering work on AI by participants of the Dartmouth Summer Research Project --- Marvin Minsky, Nathaniel Rochester, and Claude Shannon --- was more symbolic than numerical, and often used the language of logic. Recent advances in ML --- especially Deep Learning --- are more numerical than symbolic, and often use the language of probability. In this talk we shall show how to connect these two worldviews.   

Presenter: Paul Bilokon: Founder, CEO,Thalesians, Senior Quantitative Consultant, BNP Paribas  

14.30 - 15.15: Quantum Annealing for Multi-Period XVA Reverse Stress Testing 

  • Modelling
    • XVA reverse stress testing formula
    • XVA reverse stress testing as a QUBO problem ( Quadratic Unconstrained Binary Optimisation)
    • Generalisation to multi-period case
  • Optimisation using annealing
    • Quantum annealing
    • Simulated annealing
  • Applications
    • Simple portfolio of swaps
    • Firm level management

Presenter: Assad Bouayoun: Director, XVA Senior Quant, Scotiabank & Sheir Yarkoni: Data Scientist, D-Wave Systems Inc

15.15 – 15.45: Afternoon Break and Networking Opportunities

15.45 - 16.30: Second Quantization of Banks

Presenter: Christoph Burgard: 
Head of Risk Analytics For Global Markets, Bank of America Merrill Lynch

16.30 - 17.15: Deep Primal-Dual Algorithm for BSDEs: Application of Machine Learning to CVA and IM  

Presenter: Pierre Henry-Labordere: Quant, Global Markets Quantitative Research, Société Générale (to be confirmed) 

20.00: Gala Dinner

Plage Beau Rivage, Nice