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08:30: Registration and Morning Welcome Coffee


Seong Seog Lee

Director of Quant Strategy


09.00 - 09.45:  Keynote Speech

O. Ediz Ozkaya: (Machine Learning)

Presenter: O. Ediz Ozkaya: Executive Director, Machine Learning Labs, Securities, Goldman Sachs

Overcoming the Trade-off: Predictive Power vs. Expressiveness of Machine Learning Models

  • Challenges with opaque ML models
  • Controlling model behaviour in relation to tail risk
  • Expressive regularising models, native prediction confidence
  • Improving model expressiveness
  • Readiness for increasing model complexity

09.45 – 10.30From Artificial Intelligence to Machine Learning, from Logic to ProbabilityPaul Bilokon, PhD

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  

10.30 – 11.00: Morning Break and Networking Opportunities

11.00 - 11.45: "Black-box Machine Learning: Improving Transparency" Abdel Lantere

"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

11.45 - 12.30: Machine Learning - Recent Trends and Applicability to Risk and Related Areas Suhail Shergill

  • Supervised, unsupervised, Reinforcement
  • Deep learning, feature Learning, incremental learning
  • Predictive power and robustness 

Presenter: Suhail Shergill: Director | Data Science and Model Innovation, Scotiabank | Global Risk Management

12.30- 13.30: Lunch

13.30 – 14.15: Bridging the Gap Between AI and Regulatory Requirements

Presenter: Ksenia Shnyra: Senior Advisor, Deloitte

14.15 – 15.00: A Worked Example of Using Neural Networks for Time Series Prediction

Many publicly available examples of time series prediction with neural networks use fake or random data. Other examples, particularly in finance, present poorly performing models. It is very hard to learn good practices when only presented with toy examples. Instead, this talk aims to teach the full process of using a neural network for time series prediction by walking through a real problem from start to finish.  

We will begin by explaining the concrete problem we would like to solve and how to frame our problem in a way that we can model. Once we understand our problem, we will discuss how to collect the needed data. We will discuss the process of reducing our input data into important features for the model to consume. We will then learn how to use Keras to implement our neural network. Once we have a working model, we will cover some tricks to improve its performance.

At every step, we will cover problems faced while working on this model. We will show how to use data visualization to aid in model development and catch problems early. We will also cover tips for using numpy to work with time series data efficiently.  

By the end of the talk, audience members will:  

  • Know how to frame a problem in a way that a neural network can model
  • Know how to think about feature selection
  • Be familiar with the Keras API for time series predictions
  • Understand that the hardest problems come before you even get to Keras

Presenter: Joe Jevnik: Senior Software Engineer, Quantopian

15.00 - 15.30: Afternoon Break and Networking Opportunities

15.30 - 16.15: Responsible Machine Learning George A. Lentzas

This talk will discuss the variance-bias decomposition, estimation of test error and the intricacies of cross validation. I will explain what cross validation really estimates and why it is not to be used blindly.

Presenter: George A. Lentzas: Manager & Chief Data ScientistSpringfield Capital| Adjunct Associate Professor, Columbia & New York University    

16.15 - 17.00: Machine Learning Models Dr. Miquel Noguer Alonso

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

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

17.00 – 17.45: Machine Learning & Ai in Quantitative Finance Panel


  • Luis Cota: Data Scientist,Thalesians


  • O. Ediz Ozkaya: Executive Director, Machine Learning Labs, Securities, Goldman Sachs
  • Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University
  • Paul Bilokon: Founder, CEO,Thalesians, Senior Quantitative Consultant, BNP Paribas & Visiting Lecturer, Imperial College 
  • Abdel Lantere: Data Scientist, Quantitative Consultant, HSBC
  • Suhail Shergill: Director | Data Science and Model Innovation, Scotiabank 


  • 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