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 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 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.

Christoph Burgard:

Head of Risk Analytics For Global Markets, Bank of America Merrill Lynch

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

Christoph Burgard heads the Risk Analytics team for Global Markets at Bank of America Merrill Lynch, which he joined in November 2015. Prior to this he spent 16 years at Barclays, where he was leading the Equity Derivatives and XVA front office Quantitative Analytics teams for the investment bank as well as the ALM modelling area for the bank’s treasury department. Christoph was named Risk Magazine’s Quant of the Year 2015 for his pioneering work on FVA. He has a PhD in Particle Physics from Hamburg University and was a research fellow at CERN and DESY.

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.

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.

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.

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.

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 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
Derivatives Modelling and Machine Learning

William A. McGhee:

Global Head of Quantitative Analytics, NatWest Markets

William A. McGhee: Global Head of Quantitative Analytics, NatWest Markets

William started his quant career in 1994 with J.P. Morgan in the Currency Options business. In 1998 he joined Deutsche Bank where he became Global Head of FX Quantitative Analytics. He worked between 2003 and 2009 at Citi in a number of roles encompassing structuring, exotics trading and heading up the FX Quantitative Strategy Group.
He joined RBS in 2009 to run the multi-asset Hybrid Quantitative Analytics team. In his current position as Global Head of Quantitative Analytics he is responsible for all modelling within the investment bank – from electronic trading to vanilla and complex derivatives.
William holds a PhD in Mathematical Physics, is a Fellow of the Institute of Mathematics and It’s Applications and serves on the UK Parliamentary and Scientific Committee.

12.45 - 14.00
Lunch
14.00 - 14.45
Stream One: Pricing & Modelling Techniques
Inherent States of Markets 

Abstract:

Prices in markets fluctuate stochastically in a collective motion. At every instant the state of a market can be associated with the set of prices and their changes with respect to the previous observation. The time evolution of these variables can be modelled as adapting around a number of inherent fundamental states. The identification of such inherent states can provide important new tools for risk management and forecasting.

I will show how information filtering networks [1-4] built from dependency measures, both linear and non-linear, can be used to identify these inherent market states. I’ll describe how such states can used to construct predictive probabilistic models [5,6].

Bibliographic References
[1] Tumminello, Michele, Tomaso Aste, Tiziana Di Matteo, and Rosario N. Mantegna. “A tool for filtering information in complex systems.” Proceedings of the National Academy of Sciences of the United States of America 102, no. 30 (2005): 10421-10426.
[2]Aste, Tomaso, Tiziana Di Matteo, and S. T. Hyde. “Complex networks on hyperbolic surfaces.” Physica A: Statistical Mechanics and its Applications 346, no. 1-2 (2005): 20-26.
[3] Aste, Tomaso, W. Shaw, and Tiziana Di Matteo. “Correlation structure and dynamics in volatile markets.” New Journal of Physics 12, no. 8 (2010): 085009.
[4] Massara, Guido Previde, Tiziana Di Matteo, and Tomaso Aste. “Network filtering for big data: triangulated maximally filtered graph.” Journal of complex Networks 5 (2016): 161-178.
[5] W Barfuss, GP Massara, T Di Matteo, T Aste, Parsimonious modeling with information filtering networks, Physical Review E 94 (6) (2016), 062306.
[6] PF Procacci, T Aste, Forecasting market states, arXiv preprint arXiv:1807.05836.

Tomaso Aste:

Professor of Complexity Science, University College London

Tomaso Aste: Professor of Complexity Science, University College London

Tomaso Aste is Professor of Complexity Science at UCL Computer Science Department. A trained Physicist, he has substantially contributed to research in complex structures analysis, financial systems modelling and machine learning. He is passionate in the exploration of the interface between technologies on society and currently he focuses on the application of Blockchain Technologies to domains beyond digital currencies.
He is Scientific Director and Founder of the UCL Centre for Blockchain Technologies, Head and Founder of the Financial Computing and Analytics Group at UCL, Programme Director of the MSc in Financial Risk Management, Vice- Director of the Centre for doctoral training in Financial Computing & Analytics, and Member of the Board of the ESRC LSE-UCL Systemic Risk Centre.
Prior to UCL he held positions in UK and Australia. He is advising and consulting for financial institutions, banks and digital-economy companies and startups.

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: Pricing & Modelling Techniques
Topic to be confirmed

Topic to be confirmed

Ignacio Ruiz:

Founder & CEO, MoCaX Intelligence

Ignacio Ruiz: Founder & CEO, MoCaX Intelligence

Ignacio Ruiz has been the head strategist for Counterparty Credit Risk, exposure measurement, for Credit Suisse, as well as the Head of Risk Methodology, equities, for BNP Paribas. In 2010, Ignacio set up iRuiz Consulting as an independent advisory business in this field. In 2014, Ignacio founded iRuiz Technologies to develop and commercialise MoCaX Intelligence.

Ignacio has several publications in the space of quantitative risk management and pricing. He has also published a comprehensive guide to the subject of XVA Desks and Risk Management.

He holds a PhD in nano-physics from Cambridge University.

16.45 - 17.30
Stream One: Pricing & Modelling Techniques
Model Calibration with Machine Learning

Presenter to be confirmed

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 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 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.

Christoph Burgard:

Head of Risk Analytics For Global Markets, Bank of America Merrill Lynch

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

Christoph Burgard heads the Risk Analytics team for Global Markets at Bank of America Merrill Lynch, which he joined in November 2015. Prior to this he spent 16 years at Barclays, where he was leading the Equity Derivatives and XVA front office Quantitative Analytics teams for the investment bank as well as the ALM modelling area for the bank’s treasury department. Christoph was named Risk Magazine’s Quant of the Year 2015 for his pioneering work on FVA. He has a PhD in Particle Physics from Hamburg University and was a research fellow at CERN and DESY.

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.

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.

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.

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.

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 Information Compression to Improve Performance and Reduce Training Time and Hardware Requirements of Multi-Task Learning

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: 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.

15.30 - 16.00
Afternoon Break and Networking Opportunities
16.00 - 16.45
Stream Two: Quantum Computing
Topic to be confirmed

Presenter to be confirmed

16.45 - 17.30
Stream Two: Quantum Computing
Second Quantization of Banks

Second Quantization of Banks (to be confirmed)

Christoph Burgard:

Head of Risk Analytics For Global Markets, Bank of America Merrill Lynch

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

Christoph Burgard heads the Risk Analytics team for Global Markets at Bank of America Merrill Lynch, which he joined in November 2015. Prior to this he spent 16 years at Barclays, where he was leading the Equity Derivatives and XVA front office Quantitative Analytics teams for the investment bank as well as the ALM modelling area for the bank’s treasury department. Christoph was named Risk Magazine’s Quant of the Year 2015 for his pioneering work on FVA. He has a PhD in Particle Physics from Hamburg University and was a research fellow at CERN and DESY.

  • Discount Structure
  • Super early bird discount
    25% until February 8th 2019

  • Early bird discount
    10% until March 1st 2019

  • 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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