World Business StrategiesServing the Global Financial Community since 2000

Thursday 21st March

08.00 - 09.00
Registration and Morning Welcome Coffee
09.00 - 09.45
Opening Talk: Both Streams
Machine Learning in Finance
  • Use cases
    • Risk
    • Trade strategy
    • Quant Modelling
  • Behavioural Finance
  • Challenges and recent progress
  • Ethics, Institutions and Regulations

Harsh Prasad:

Vice President, Morgan Stanley

Harsh Prasad: Vice President, Morgan Stanley 

Harsh started his career as a programmer working on various search and pattern recognition algorithms including AI techniques, across radio astrophysics, bioinformatics and speech recognition. He then transitioned to the financial risk domain and for the last decade has worked in many regulatory jurisdictions with banks and finance companies as well as consulting firms focussed on quant modelling. In this period he has applied Machine Learning techniques to behavioural modelling for ALM, mortgage risk modelling, derivatives pricing, time series outlier detection and risk data management. He has been a guest faculty with B schools and is currently authoring a book titled ‘Machine Learning for Finance’.

09.45 - 10.45
Panel: Both Streams
Machine Learning, AI & Quantum Computing in Quantitative Finance Panel

Topics:

  • 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

Moderator:

  • Harsh Prasad: Vice President, Morgan Stanley 

Panellists:

Harsh Prasad:

Vice President, Morgan Stanley

Harsh Prasad: Vice President, Morgan Stanley 

Harsh started his career as a programmer working on various search and pattern recognition algorithms including AI techniques, across radio astrophysics, bioinformatics and speech recognition. He then transitioned to the financial risk domain and for the last decade has worked in many regulatory jurisdictions with banks and finance companies as well as consulting firms focussed on quant modelling. In this period he has applied Machine Learning techniques to behavioural modelling for ALM, mortgage risk modelling, derivatives pricing, time series outlier detection and risk data management. He has been a guest faculty with B schools and is currently authoring a book titled ‘Machine Learning for Finance’.

Alexei Kondratyev:

Managing Director, Head of Data Analytics, Standard Chartered Bank

Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank

In his role as Managing Director and Head of Data Analytics at Standard Chartered Bank, Alexei is responsible for providing data analytics services to Financial Markets sales and trading.

He joined Standard Chartered Bank in 2010 from Barclays Capital where he managed a model development team within Credit Risk Analytics. Prior to joining Barclays Capital in 2004, he was a senior quantitative analyst at Dresdner Bank in Frankfurt.

Alexei holds MSc in Theoretical Nuclear Physics from the University of Kiev and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine.

Claudi Ruiz Camps:

Machine Learning Specialist, ABN AMRO Clearing Bank

Claudi Ruiz Camps: Machine Learning Specialist, ABN AMRO Clearing Bank

Claudi has studied Physics at Autonomous University of Barcelona and a master’s degree in Automatic Control and Robotics at Polytechnic University of Catalonia. He has been doing research in Machine Learning for the industry since 2015 and currently he is working at ABN AMRO Clearing Bank as a Machine Learning Specialist. His domain of expertise is unsupervised learning and he is currently tackling problems such as unsupervised anomaly detection, information compression, clustering and time series forecast by using approaches within the framework of variational autoencoders, recurrent neural networks and generative adversarial networks among others.

Artur Sepp:

Head of Research, Quantica Capital AG

Artur Sepp: Head of Research, Quantica Capital AG

Artur Sepp is Head of Research at Quantica Capital AG in Zurich focusing on systematic data-driven trading strategies. Artur has extensive experience working as a Quantitative Strategist in leading roles since 2006. Prior to joining Quantica, Artur worked at Julius Baer in Zurich developing algorithmic solutions and strategies for the wealth management and portfolio advisory. Before, Artur worked as a front office quant strategist for equity and credit derivatives trading at Bank of America Merrill Lynch in London and Merrill Lynch in New York.  Artur has a PhD in Statistics, an MSc in Industrial Engineering from Northwestern University, and a BA in Mathematical Economics. Artur’s research area and expertise are on econometric data analysis, machine learning, and computational methods with their applications for quantitative trading strategies and asset allocation. He is the author and co-author of several research articles on quantitative finance published in leading journals and he is known for his contributions to stochastic volatility and credit risk modelling. Artur is a member of the editorial board of the Journal of Computational Finance.

Blanka Horvath:

Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka Horvath: Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka is a Honorary Lecturer in the Department of Mathematics at Imperial College London and a Lecturer at King’s College London. Her research interests are in the area of Stochastic Analysis and Mathematical Finance.

Her interests include asymptotic and numerical methods for option pricing, smile asymptotics for local- and stochastic volatility models (the SABR model and fractional volatility models in particular), Laplace methods on Wiener space and heat kernel expansions.

Blanka completed her PhD in Financial Mathematics at ETHZürich with Josef Teichmann and Johannes Muhle-Karbe. She holds a Diploma in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong.

Alison B. Lowndes:

Artificial Intelligence DevRel | EMEA, NVIDIA

Alison B. Lowndes: Artificial Intelligence DevRel | EMEA, NVIDIA

Joining in 2015, Alison spent her first 18 months with NVIDIA as a Deep Learning Solutions Architect and is now responsible for NVIDIA’s Artificial Intelligence Developer Relations in the EMEA region (Europe, Middle East, Africa). She is a mature graduate in Artificial Intelligence combining technical and theoretical computer science with a physics background & over 20 years of experience in international project management, entrepreneurial activities and the internet. She consults on a wide range of AI applications, including planetary defence with NASA, ESA & the SETI Institute and continues to manage the community of AI & Machine Learning researchers around the world, remaining knowledgeable in state of the art across all areas of research. She also travels, advises on & teaches NVIDIA’s GPU Computing platform, around the globe.
Twitter: @AlisonBLowndes.

10.45 - 11.15
Morning Break and Networking Opportunities
Stream Chair:

Harsh Prasad:

Vice President, Morgan Stanley

Harsh Prasad: Vice President, Morgan Stanley 

Harsh started his career as a programmer working on various search and pattern recognition algorithms including AI techniques, across radio astrophysics, bioinformatics and speech recognition. He then transitioned to the financial risk domain and for the last decade has worked in many regulatory jurisdictions with banks and finance companies as well as consulting firms focussed on quant modelling. In this period he has applied Machine Learning techniques to behavioural modelling for ALM, mortgage risk modelling, derivatives pricing, time series outlier detection and risk data management. He has been a guest faculty with B schools and is currently authoring a book titled ‘Machine Learning for Finance’.

11.15 - 12.00
Stream One: Pricing & Modelling Techniques
Derivatives Pricing with a Machine Learning Approach
  • Motivation — Non-Parametric Option Pricing
  • Review of Machine Learning Techniques
  • A Mathematical Introduction to Neural Networks
  • Universal Representation Theorem
  • Deep Pricing Learning Theory
  • Practical Implementation
  • Numerical Applications

Youssef Elouerkhaoui:

Managing Director, Head of Credit Derivatives, CITI

Youssef Elouerkhaoui, Managing Director, Head of Credit Derivatives, CITI  

Youssed Elouerkhaoui is the global Head of Credit Quantitive Analysis at Citi. His group supports all aspects of modelling and product development across desks, thais includes: Flow Credit Trading, Correlation Trading, CDOs, Exotics and Emering Markets.

He also supports CVA, Funding and Regulatory Capital for Credit Markets. Prior to this, he was a Director in the Fixed Income Derivatives Quantitative Research Group at UBS, where he was in charge of developing and implementing models for the Structured Credit Desk. Before joining UBS, Youssef was a Quantitative Research Analyst at Credit Lyonnais supporting the Interest Rates Exotics business. He has also worked as a Senior Consultant in the Risk Analytics and Research Group at Ernst & Young. He is a graduate of Ecole Centrale Paris and he holds a PhD in Mathematics from Paris-Dauphine University.

12.00 - 12.45
Stream One: Pricing & Modelling Techniques
A Deep Learning Approach to Exotic Option Pricing under LSVol
  • The market standard for the pricing and risk management of complex derivatives within the Foreign Exchange markets uses a local-stochastic volatility (LSVol) model.
  • This type of model can better capture relevant market dynamics but is computationally very expensive.
  • We use a Deep learning approach to value path-dependent Exotic Options under LSVol, achieving high degree of accuracy (to production standard)
  • We’ll explore this innovative approach, which is a radical departure from the traditional quantitative finance methodology prevalent in banks

Katia Babbar:

AI Wealth Technologies Founder & Visiting Research Fellow, Oxford Mathematical Institute

Katia Babbar: AI Wealth Technologies Founder & Visiting Research Fellow, Oxford Mathematical Institute

12.45 - 14.00
Lunch
14.00 - 14.45
Stream One: Pricing & Modelling Techniques
tcapy: Designing a FX TCA Library in Python

The talk will introduce the topic of TCA (transaction cost analysis) and the various approaches which can be used to do it. It will also outline the typical metrics which are used in TCA, and will include an interactive demo of tcapy to illustrate them. Later, the talk will focus on the software design aspects of tcapy, along with the Python libraries used. There will also be section on the various Python tips & tricks which were used to speed up the computation.

Saeed Amen

Founder: Cuemacro

Saeed Amen: Founder: Cuemacro

Saeed has a decade of experience creating and successfully running systematic trading models at Lehman Brothers and Nomura. He is the founder of Cuemacro, Cuemacro is a company focused on understanding macro markets from a quantitative perspective. He is the author of ‘Trading Thalesians – What the ancient world can teach us about trading today’ (Palgrave Macmillan), and graduated with a first class honours master’s degree from Imperial College in Mathematics& Computer Science.

14.45 - 15.30
Stream One: Pricing & Modelling Techniques
Identification and Forecast of Market Regimes using Machine Learning
  • Applying Hidden Markov Models (HMM) to identify market regimes (bull/bear/range etc)
  • Specification and estimation of HMMs using Unsupervised Learning
  • Forecasting of likelihoods of regimes at different horizons
  • Applications to systematic trading strategies

Artur Sepp:

Head of Research, Quantica Capital AG

Artur Sepp: Head of Research, Quantica Capital AG

Artur Sepp is Head of Research at Quantica Capital AG in Zurich focusing on systematic data-driven trading strategies. Artur has extensive experience working as a Quantitative Strategist in leading roles since 2006. Prior to joining Quantica, Artur worked at Julius Baer in Zurich developing algorithmic solutions and strategies for the wealth management and portfolio advisory. Before, Artur worked as a front office quant strategist for equity and credit derivatives trading at Bank of America Merrill Lynch in London and Merrill Lynch in New York.  Artur has a PhD in Statistics, an MSc in Industrial Engineering from Northwestern University, and a BA in Mathematical Economics. Artur’s research area and expertise are on econometric data analysis, machine learning, and computational methods with their applications for quantitative trading strategies and asset allocation. He is the author and co-author of several research articles on quantitative finance published in leading journals and he is known for his contributions to stochastic volatility and credit risk modelling. Artur is a member of the editorial board of the Journal of Computational Finance.

15.30 - 16.00
Afternoon Break and Networking Opportunities
16.00 - 16.45
Stream One: Artificial Intelligence
Using Artificial Intelligence to measure Sustainable Development Goals

Miquel Noguer Alonso:

Co – Founder and Chief Science Officer, Artificial Intelligence Finance Institute – AIFI

Miquel Noguer Alonso: Co – Founder and Chief Science Officer, Artificial Intelligence Finance Institute – AIFI

Miquel Noguer is a financial markets practitioner with more than 20 years of experience in asset management, he is currently Head of Development at Global AI ( Big Data Artificial Intelligence in Finance company ) and Head on Innovation and Technology at IEF.

He worked for UBS AG (Switzerland) as Executive Director.for the last 10 years. He worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006.

He is professor of Big Data in Finace at ESADE and Adjunct Professor at Columbia University teaching Asset Allocation, Big Data in Finance and Fintech. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain).

16.45 - 17.30
Stream One: Artificial Intelligence
Quantifying Model Uncertainty with Artificial Intelligence
  • Defining model risk and model uncertainty
  • Overview of relevant regulatory frameworks
  • Measuring uncertainty with ML
  • Model risk of AI

Jos Gheerardyn:

Co-founder and CEO, Yields.io

Jos Gheerardyn: Co-founder and CEO of Yields.io

Jos is the co-founder and CEO of Yields.io. Prior to his current role he has been active in quantitative finance both as a manager and as an analyst. Over the past 15 years he has been working with leading international investment banks as well as with award winning start-up companies. He is the author of multiple patents applying quantitative risk management techniques on imbalance markets. Jos holds a PhD in superstring theory from the University of Leuven.

Thursday 21st March

08.00 - 09.00
Registration and Morning Welcome Coffee
09.00 - 09.45
Opening Talk: Both Streams
Machine Learning in Finance
  • Use cases
    • Risk
    • Trade strategy
    • Quant Modelling
  • Behavioural Finance
  • Challenges and recent progress
  • Ethics, Institutions and Regulations

Harsh Prasad:

Vice President, Morgan Stanley

Harsh Prasad: Vice President, Morgan Stanley 

Harsh started his career as a programmer working on various search and pattern recognition algorithms including AI techniques, across radio astrophysics, bioinformatics and speech recognition. He then transitioned to the financial risk domain and for the last decade has worked in many regulatory jurisdictions with banks and finance companies as well as consulting firms focussed on quant modelling. In this period he has applied Machine Learning techniques to behavioural modelling for ALM, mortgage risk modelling, derivatives pricing, time series outlier detection and risk data management. He has been a guest faculty with B schools and is currently authoring a book titled ‘Machine Learning for Finance’.

09.45 - 10.45
Panel: Both Streams
Machine Learning, AI & Quantum Computing in Quantitative Finance Panel

Topics:

  • 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

Moderator:

  • Harsh Prasad: Vice President, Morgan Stanley 

Panellists:

Harsh Prasad:

Vice President, Morgan Stanley

Harsh Prasad: Vice President, Morgan Stanley 

Harsh started his career as a programmer working on various search and pattern recognition algorithms including AI techniques, across radio astrophysics, bioinformatics and speech recognition. He then transitioned to the financial risk domain and for the last decade has worked in many regulatory jurisdictions with banks and finance companies as well as consulting firms focussed on quant modelling. In this period he has applied Machine Learning techniques to behavioural modelling for ALM, mortgage risk modelling, derivatives pricing, time series outlier detection and risk data management. He has been a guest faculty with B schools and is currently authoring a book titled ‘Machine Learning for Finance’.

Alexei Kondratyev:

Managing Director, Head of Data Analytics, Standard Chartered Bank

Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank

In his role as Managing Director and Head of Data Analytics at Standard Chartered Bank, Alexei is responsible for providing data analytics services to Financial Markets sales and trading.

He joined Standard Chartered Bank in 2010 from Barclays Capital where he managed a model development team within Credit Risk Analytics. Prior to joining Barclays Capital in 2004, he was a senior quantitative analyst at Dresdner Bank in Frankfurt.

Alexei holds MSc in Theoretical Nuclear Physics from the University of Kiev and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine.

Claudi Ruiz Camps:

Machine Learning Specialist, ABN AMRO Clearing Bank

Claudi Ruiz Camps: Machine Learning Specialist, ABN AMRO Clearing Bank

Claudi has studied Physics at Autonomous University of Barcelona and a master’s degree in Automatic Control and Robotics at Polytechnic University of Catalonia. He has been doing research in Machine Learning for the industry since 2015 and currently he is working at ABN AMRO Clearing Bank as a Machine Learning Specialist. His domain of expertise is unsupervised learning and he is currently tackling problems such as unsupervised anomaly detection, information compression, clustering and time series forecast by using approaches within the framework of variational autoencoders, recurrent neural networks and generative adversarial networks among others.

Artur Sepp:

Head of Research, Quantica Capital AG

Artur Sepp: Head of Research, Quantica Capital AG

Artur Sepp is Head of Research at Quantica Capital AG in Zurich focusing on systematic data-driven trading strategies. Artur has extensive experience working as a Quantitative Strategist in leading roles since 2006. Prior to joining Quantica, Artur worked at Julius Baer in Zurich developing algorithmic solutions and strategies for the wealth management and portfolio advisory. Before, Artur worked as a front office quant strategist for equity and credit derivatives trading at Bank of America Merrill Lynch in London and Merrill Lynch in New York.  Artur has a PhD in Statistics, an MSc in Industrial Engineering from Northwestern University, and a BA in Mathematical Economics. Artur’s research area and expertise are on econometric data analysis, machine learning, and computational methods with their applications for quantitative trading strategies and asset allocation. He is the author and co-author of several research articles on quantitative finance published in leading journals and he is known for his contributions to stochastic volatility and credit risk modelling. Artur is a member of the editorial board of the Journal of Computational Finance.

Blanka Horvath:

Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka Horvath: Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka is a Honorary Lecturer in the Department of Mathematics at Imperial College London and a Lecturer at King’s College London. Her research interests are in the area of Stochastic Analysis and Mathematical Finance.

Her interests include asymptotic and numerical methods for option pricing, smile asymptotics for local- and stochastic volatility models (the SABR model and fractional volatility models in particular), Laplace methods on Wiener space and heat kernel expansions.

Blanka completed her PhD in Financial Mathematics at ETHZürich with Josef Teichmann and Johannes Muhle-Karbe. She holds a Diploma in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong.

Alison B. Lowndes:

Artificial Intelligence DevRel | EMEA, NVIDIA

Alison B. Lowndes: Artificial Intelligence DevRel | EMEA, NVIDIA

Joining in 2015, Alison spent her first 18 months with NVIDIA as a Deep Learning Solutions Architect and is now responsible for NVIDIA’s Artificial Intelligence Developer Relations in the EMEA region (Europe, Middle East, Africa). She is a mature graduate in Artificial Intelligence combining technical and theoretical computer science with a physics background & over 20 years of experience in international project management, entrepreneurial activities and the internet. She consults on a wide range of AI applications, including planetary defence with NASA, ESA & the SETI Institute and continues to manage the community of AI & Machine Learning researchers around the world, remaining knowledgeable in state of the art across all areas of research. She also travels, advises on & teaches NVIDIA’s GPU Computing platform, around the globe.
Twitter: @AlisonBLowndes.

10.45 - 11.15
Morning Break and Networking Opportunities
Stream Chair:

Alexei Kondratyev:

Managing Director, Head of Data Analytics, Standard Chartered Bank

Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank

In his role as Managing Director and Head of Data Analytics at Standard Chartered Bank, Alexei is responsible for providing data analytics services to Financial Markets sales and trading.

He joined Standard Chartered Bank in 2010 from Barclays Capital where he managed a model development team within Credit Risk Analytics. Prior to joining Barclays Capital in 2004, he was a senior quantitative analyst at Dresdner Bank in Frankfurt.

Alexei holds MSc in Theoretical Nuclear Physics from the University of Kiev and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine.

11.15 - 12.00
Stream Two: Hardware & GPUs
Who Cares about the Platform?? (Spoiler alert - you should)

What’s the point in a super accurate predictions – if they are a day too late? Or so-so insights – because you’ve not had time to train using all your data? Wouldn’t it have been good to try some more potential models – to check you’ve got the best option?

Your choice of platform can have a large impact on the amount of data you can process and the range and accuracy of models you train. Come and hear about your options on the POWER platform – acceleration using GPUs / FPGA, optimised open source frameworks, wide ranging software ecosystem – and check you aren’t missing out on something which could increase your productivity and accuracy.

Dr Mandie Quartly:

Global Tech Lead, IBM POWER Ecosystem Development

Dr Mandie Quartly, Global Tech Lead, IBM POWER Ecosystem Development

Focused on the creation and growth of strategic relationships with key software organisations. In particular those using AI capabilities and looking to enable end users to gain timely insights from their data. Mandie’s background is Linux, Power Systems and High Performance Computing focused, specialising in the design and implementation of high performance Linux-based systems. Mandie has an MBA and a Ph.D. in Astrophysics.

12.00 - 12.45
Stream Two: Hardware & GPUs
Fuelling the Artificial Intelligence Revolution with Gaming

Abstract:

Artificial Intelligence is impacting all areas of society, from healthcare and transportation to smart cities and energy. AI won’t be an industry, it will be part of every industry. NVIDIA invests both in internal research and platform development to enable its diverse customer base, across gaming, VR, AR, AI, robotics, graphics, rendering, visualisation, HPC, healthcare & more. Alison’s talk will introduce the hardware and software platform at the heart of this Intelligent Industrial Revolution: NVIDIA GPU Computing. She’ll provide insights into how academia, enterprise and startups are applying AI, as well as offer a glimpse into state-of-the-art research from world-wide labs & internally at NVIDIA, demoing, for example, the combination of robotics with VR and AI in an end-to-end simulator to train intelligent machines.
Beginners might like to try our free online 40-minute “Electives” using GPU’s in the cloud: www.nvidia.co.uk/dli

Alison B. Lowndes:

Artificial Intelligence DevRel | EMEA, NVIDIA

Alison B. Lowndes: Artificial Intelligence DevRel | EMEA, NVIDIA

Joining in 2015, Alison spent her first 18 months with NVIDIA as a Deep Learning Solutions Architect and is now responsible for NVIDIA’s Artificial Intelligence Developer Relations in the EMEA region (Europe, Middle East, Africa). She is a mature graduate in Artificial Intelligence combining technical and theoretical computer science with a physics background & over 20 years of experience in international project management, entrepreneurial activities and the internet. She consults on a wide range of AI applications, including planetary defence with NASA, ESA & the SETI Institute and continues to manage the community of AI & Machine Learning researchers around the world, remaining knowledgeable in state of the art across all areas of research. She also travels, advises on & teaches NVIDIA’s GPU Computing platform, around the globe.
Twitter: @AlisonBLowndes.

12.45 - 14.00
Lunch
14.00 - 14.45
Stream Two: Hardware & GPUs
Unsupervised Learning for Multi-Task Learning on Trade Data

ABN AMRO Clearing Bank works with considerably large amounts of data every day, and designs and implements in production deep learning models together with hyper-parameter optimization models to approach some of their business cases. Our talk will be focused on our main model with the goal of projecting the raw data onto a meaningful lower-dimensional space. This projection is beneficial for our other models, which have different tasks, in three ways:

  1. The hyper-parameter space of the other models can be modeled more rapidly. The other models no longer need to learn first order representations since those are given by the main model, which means that their training time is reduced.
  2. The other models can be smaller now as well, so less GPU space is required.
  3. Better performance and less tendency to overfit. The representations learnt by the main model (unsupervised learning) are, in principle, richer than if they would have to be learnt for specific tasks (supervised learning).

Claudi Ruiz Camps:

Machine Learning Specialist, ABN AMRO Clearing Bank

Claudi Ruiz Camps: Machine Learning Specialist, ABN AMRO Clearing Bank

Claudi has studied Physics at Autonomous University of Barcelona and a master’s degree in Automatic Control and Robotics at Polytechnic University of Catalonia. He has been doing research in Machine Learning for the industry since 2015 and currently he is working at ABN AMRO Clearing Bank as a Machine Learning Specialist. His domain of expertise is unsupervised learning and he is currently tackling problems such as unsupervised anomaly detection, information compression, clustering and time series forecast by using approaches within the framework of variational autoencoders, recurrent neural networks and generative adversarial networks among others.

14.45 - 15.30
Stream Two:
Deep Learning for Automatic Feature Generation for Time Series Forecasting

Abstract:

There is an attempt to construct a DeepNN architecture that enables to automatically extract relevant features at relevant timestamps in the past for prediction of future price movement. The approach is based on the paper “DeepLOB: Deep convolutional Neural Networks for Limit Order Books” by Zhihao Zhang, Stefan Zohren and Stephen Roberts. Using the proposed techniques we try to automatically generate features from Limit Order Book for different asset classes and compare the generated features with those that are hand-crafted and give good results in prediction.

Ivan Zhdankin:

Associate, Quantitative Analyst, JPMorgan Chase & Co

Ivan Zhdankin: Associate, Quantitative Analyst, JPMorgan Chase & Co

Ivan Zhdankin is a quantitative researcher with experience in diverse areas of quantitative finance, including risk modelling, XVA, and electronic trading across asset classes, including commodity futures and G10 and emerging market currencies. Ivan was consulting various banks in quantitative modeling and has recently joined JP Morgan as a quantitative analyst. He has become one of the first researchers to generate convincing results in electronic alpha with neural nets. He has a solid mathematical background from New Economic School and Moscow State University, where he studied under the celebrated Albert Shiryaev, one of the developers of modern probability theory.

15.30 - 16.00
Afternoon Break and Networking Opportunities
16.00 - 16.45
Stream Two: Quantum Computing
Quantum Machine Learning
  • Training strong classifiers with quantum annealing
  • Quantum Boltzmann Machine

Alexei Kondratyev:

Managing Director, Head of Data Analytics, Standard Chartered Bank

Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank

In his role as Managing Director and Head of Data Analytics at Standard Chartered Bank, Alexei is responsible for providing data analytics services to Financial Markets sales and trading.

He joined Standard Chartered Bank in 2010 from Barclays Capital where he managed a model development team within Credit Risk Analytics. Prior to joining Barclays Capital in 2004, he was a senior quantitative analyst at Dresdner Bank in Frankfurt.

Alexei holds MSc in Theoretical Nuclear Physics from the University of Kiev and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine.

16.45 - 17.30
Stream Two: Quantum Computing
Financial Industry Applications of Quantum Computing

Abstract:

Quantum computing is an emergent technology with the potential to increase computational capabilities for organisations. The financial applications of quantum computing should increase profit, reduce risk and enhance the customer experience. This presentation will cover:

  • Technology and landscape.
  • Important quantum algorithms.
  • Applicability of quantum algorithms to financial use cases.
  • Three developed examples of financial applications using quantum computing, involving Monte Carlo simulation, approximate optimisation, and machine learning algorithms.

David Garvin:

Principal Researcher Quantitative Analysis, QxBranch

 

David Garvin:  Principal Researcher Quantitative Analysis, QxBranch

David focusses on researching, developing and implementing financial industry applications of quantum computing. QxBranch delivers predictive analytics, illuminated by Explainable Data Science, leveraging the emerging potential of Quantum Computing.

David has over 20 years experience as a Front-Office Quant in the Finance Industry. Previously, he has been the Global Head of Quantitative Analysis at the Commonwealth Bank of Australia. Prior to that, he was a Director at Deutsche Bank and a Quant Analyst at Morgan Grenfell. He has covered all asset classes and been involved in management, modelling, risk and analytics, derivatives and structured products, machine learning and electronic trading.

David holds a PhD in Artificial Intelligence from Cambridge University and an MBA (Exec) from the Australian Graduate School of Management. He has authored articles in finance, computing, physics and engineering.

 

  • Discount Structure
  • Special Offer
    When two colleagues attend the 3rd goes free!

  • Conference + Workshop
    £150 Discount

  • 70% Academic Discount
    (FULL-TIME Students Only)

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