Event Agenda

Agenda

Course Agenda

Day One

09:00

Registration and refreshments

09.30

Big Data in the Finance Landscape

  • Big data in the finance landscape: Financial modelling, data governance, integration, NoSQL, batch and real-time computing and storage
  • Infrastructure and technology
  • New data sources
  • Modern data analysis - Structured/unstructured data and new models

Speaker: Ksenia Shnyra, Managing Partner, Exeter Consulting and Capital Management 

11:00

Morning break

11:30

Machine Learning Models

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

Speaker: Jésus Caldéron, Co-Founder and Director, Gravito  

1:00

Lunch

2:00

Machine Learning in Finance: Practice

  • Pros and cons of applying ML to investing 
  • Importance of features selection
  • Subtleties of applying ML to investing 
  • Where to start? 

Speaker: Gordon Ritter, Adjunct Professor, Baruch College & New York University and Professor of Practice, Rutgers University   

3:30

Afternoon break

4:00

AI Interpretability

  • Deep Learning models often perform remarkable tasks and are highly accurate
  • But they are also highly opaque: they work, but we often don’t understand how
  • What are the risks of deploying uninterpretable models?
  • Discuss the concepts of AI Security and AI Safety
  • Introduce Adversarial Examples to illustrate one risk
  • The thrust of the talk will be to teach techniques to better interpret Deep Learning models

Speaker: Ken Perry, Consultant in Risk and Quantamental Investing, CRO, formerly of Och Ziff

5:30

End of day one

Day Two

09:00

Refreshments

09:30

Quantum Machine Learning

  • This session will analyse the emerging techniques applicable to quantum computing and its applications.

Speaker: Steve Yalovitser, Co-Founder, New York Quantum Computing Meet-up and Director, XVA Quant Core Lead, Wells Fargo

11:00

Morning break

11:30

Machine Learning and Portfolio Construction

  • Data science & machine learning in quantitative finance
  • New tools and techniques in large-scale machine learning and analytics
  • An overview of developments in data science and machine learning through the context of the needs of the quantitative finance industry
  • Multi-period portfolio optimization

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

1:00

Lunch

2:00

Deep Learning Techniques for Derivatives Pricing and Analytics 

  • We review some new approaches from research and literature and Wells Fargo's work to apply deep learning techniques and computational graph techniques (including algorithmic differentiation) to the solution of high-dimensional forward-backward SDE and PDE in derivative pricing, present some fundamental ideas, applications to derivatives pricing and analytics with some results, and some current and planned extensions.

Speaker: Bernhard Heintzsch, Head of Model, Library, and Tools Development (Corporate Model Risk), Wells Fargo 

3:30

Afternoon break

4:00

Machine Learning and Model Risk

  • Model Risk Management: what is it?
  • The 3 challenges of using machine learning for investment strategies
  • Practical solutions when using machine learning for investment strategies

Speaker: Ben Steiner, Global Fixed Income, BNP Paribas Asset Management 

5:30

End of course