Machine Learning: Latest Use Cases, NLP, Data Access and Aggregation

Machine Learning: Latest Use Cases, NLP, Data Access and Aggregation

Post-conference workshop: July 19th

Machine Learning: Latest Use Cases, NLP, Data Access and Aggregation 

Machine Learning workshop is designed to provide  in-depth content on practical and most exciting ML/AI applications in financial services. We will host multiple senior speakers who will share their latest research in the classroom setting, this year, with particular focus on practical use cases, NLP tools, data access and aggregation.

Why attend the workshop:

  • Gain deeper insights into the latest industry applications and what progress has been achieved since the ML boom;
  • Understand how to access Big Data and alternative data in financial markets and how to work with it efficiently when building your models;
  • NLP applications: How are quants successfully harnessing data?
  • Discuss ML/AI limitations and how industry view interpretability issues;

08:20

REGISTRATION & REFRESHMENTS 

9.00

Model risk management for alpha strategies created with deep learning  

  • Understanding the challenges of using deep learning to build alpha generation strategies
  • Model Risk Management to detect when machine learning strategies are not performing as intended.
  • Can you model a constantly moving market? When DL should (and should not) be used
Ben Steiner, Global Fixed Income, BNP PARIBAS ASSET MANAGEMENT

10:30

MORNING BREAK

11:00

Machine learning in trading and portfolio optimization

  • The impact ML has on trading and portfolio optimization
  • Case study: Solving hedging problems with reinforcement learning
  • Case study 2: Multi-period t-cost aware portfolio optimization

Petter Kolm, Director of the Mathematics in Finance Program and Clinical Professor, Courant Institute of Mathematical Sciences, New York University

12:30

LUNCH & OPPORTUNITY TO NETWORK

1:30

I. Quantum Machine Learning

This session will analyze the emerging techniques applicable to quantum computing and its applications

Steve Yalovitser, Co-Founder, New York Quantum Computing Meet-up and Director, XVA Quant Core Lead, WELLS FARGO
 

II. Alternative Data and ML: New oil, old toil.

  • Big data turned statistics into ML
  • Data quality - point of failure of ML projects
  • Data engineering - make it or break it
  • ML doesn’t give right answers to wrong questions

Olga Kokareva, Head of Data Sourcing and Strategy, QUANTSTELLATION

3:00

AFTERNOON BREAK

3:30

From classic neural nets via quantum walks to quantum computing – genesis of paradigms and applications

  • Classic neural nets
  • Quantum walks
  • Versatile applications across technologies and finance

Mark Syrkin, Financial Institution Supervision  Group, FEDERAL RESERVE BANK OF NEW YORK
Heydar Qasimov, Senior Risk Management Specialist, FEDERAL RESERVE BANK OF CHICAGO 

5:00

END OF THE SEMINAR