World Business StrategiesServing the Global Financial Community since 2000

Main Conference Day 2: Friday 21st October

08.30 – 09.00: Morning Welcome Coffee

Chair: Volatility, Pricing & Modelling

Mariano Zeron:

Head of Research and Development: MoCaX Intelligence

Mariano Zeron: Head of Research and Development: MoCaX Intelligence

Mariano leads our Research & Development work. He has vast experience in Chebyshev Spectral Decomposition, machine-learning and related disciplines, and their application to quantitative problems in the financial markets. Mariano holds a Ph.D. in Mathematics from Cambridge University.

Volatility, Pricing & Modelling Stream

09.00 – 09.45: Portfolio Replication for Valuation Adjustments

Milena Imamovic-Tomasovic:

Global Head of Product Valuation Methodologies and VCG Digital, Citi

Milena Imamovic-Tomasovic: Global Head of Product Valuation Methodologies and VCG Digital, Citi

Milena Imamovic-Tomasovic is a quantitative finance professional with over fifteen years of experience in banking. Her current role is Global Head of product valuation Methodologies and VCG Digital in Citi. Prior to that, she was Head of Business-Aligned Valuation Methodology within Global Valuation Group team and Head of CVA and Funding Methodology within GVG Methodology in Deutsche Bank. Before that Head of Analytics, EMEA at HSBC where she headed a cross-asset Valuation Control quant team. Before joining HSBC, Milena worked at TD Securities as a model validation and subsequently front office equity quant. She holds a Ph.D in theoretical physics from the University of Toronto.

Volatility, Pricing & Modelling Stream

09.45 – 10.30: Practical Quantum Computing in Finance

  • General considerations
    • General principles of quantum computing
    • Type of machines and algorithms
  • HPC perspective
    • Mapping a financial problem to an algorithm
    • Mapping an algorithm to a particular infrastructure (distributed hardware)
    • Where could quantum computing fit ? A concrete example
  • Quant perspective
    • Portfolio optimisation
    • Reverse stress testing

Assad Bouayoun:

XVA and Credit Derivative Quant, Daiwa Capital Markets

Assad Bouayoun: XVA and Credit Derivative Quant, Daiwa Capital Markets

Assad Bouayoun is a senior XVA Quantitative Analyst with more than 15 years’ experience in leading banks. He has designed industry standard hedging and pricing systems, first in equity derivative at Commerzbank, then in credit derivatives at Credit Agricole, in XVA at Lloyds in Model Validation at RBS in Model Development. Assad has an extensive experience in developing enterprise wide analytics to improve the financial management of derivative portfolios, in particular large scale hybrid Monte-Carlo and Exposure computation. Assad is currently building the prototype of a new XVA platform integrating cutting-edge technologies (GPU, Cloud computing) and numerical methods (AAD) to enable fast and accurate XVA and sensitivities computation. He holds a MSc in Mathematical Trading and Finance from CASS business school and a Master in Applied Mathematics and Computer Science from Université de Technologie de Compiegne (France).

10.30 – 11.00: Morning Break and Networking Opportunities

Volatility, Pricing & Modelling Stream

11.00 – 11.45: Fast Pricing using Tensor Network Methods

Abstract: Tensor network methods are generally useful for high-dimensional problems, can outperform Monte Carlo and (partially) break the quantum advantage associated with amplitude estimation. A framework of interpolation-based tensor networks will be presented and practical performance gains demonstrated for pricing.

Sebastian Cassel:

Head of Valuation Model Risk, BNP Paribas

Sebastian Cassel: Head of Valuation Model Risk, BNP Paribas

Sebastian Cassel leads the Valuation Model Risk team at BNP Paribas covering model validation for trading book valuations including xVA. Previously, he worked in the Quantitative Research Centre at Royal Bank of Scotland Group developing market & counterparty risk models. Sebastian holds a D.Phil. in theoretical physics from Oxford University and M.Sci. from Cambridge University.

Volatility, Pricing & Modelling Stream

11.45 – 12.30: Revaluation challenge of FRTB-IMA. A focus on Equity autocallables and FX TARFs

We show the results of applying the Orthogonal Chebyshev Sliding Technique on a portfolio of Autocallables and a portfolio of FX TARFs within the context of FRTB-IMA, using the real systems of a bank.

The results obtained pass the PLA tests while showing computational savings of around 95% compared to the capital calculation uses the pricing models in Front Office.

  • Pricing challenge in FRTB-IMA
  • Theoretical properties of Chebyshev Tensors
  • By-passing curse of dimension with Chebyshev Sliders
  • Numerical results

Mariano Zeron:

Head of Research and Development: MoCaX Intelligence

Mariano Zeron: Head of Research and Development: MoCaX Intelligence

Mariano leads our Research & Development work. He has vast experience in Chebyshev Spectral Decomposition, machine-learning and related disciplines, and their application to quantitative problems in the financial markets. Mariano holds a Ph.D. in Mathematics from Cambridge University.

12.30 – 13.30: Lunch

Chair: Volatility, Pricing & Modelling

Andrew McClelland: 

Director, Quantitative Research, Numerix

Andrew McClelland: Director, Quantitative Research, Numerix

Andrew McClelland’s work at Numerix focuses on counterparty credit risk issues including valuation adjustments and counterparty exposure production for structured products. He also works on numerical methods for efficient production of risk profiles under real-world measures.

Andrew received his Ph.D. in finance at the Queensland University of Technology in financial econometrics. His research involved markets exhibiting crash feedback, option pricing, and parameter estimation using particle filtering methods. His work has been published in the Journal of Banking and Finance, the Journal of Econometrics, and the Journal of Business and Economic Statistics.

Volatility, Pricing & Modelling Stream

13.30 – 14.15: Joint Modelling of CMS rates in a Risk-Free Rate framework

Elias Daboussi:

Quantitative Analyst, Bank of America Merrill Lynch

Elias Daboussi: Quantitative Analyst, Bank of America Merrill Lynch

Elias Daboussi is a quantitative analyst at Bank of America since 2016. After graduating from University Paris-Diderot and Supelec in 2014, he has specialized in the Rates and Hybrids area, first in the Model Risk Management Group, and now as part of the Quantitative Strategies Group.

Volatility, Pricing & Modelling Stream

14.15 – 15.00: Counterparty Risk Pricing of Exotic Equity Products

Behnaz Zargar:

Senior Quantitative Analyst, Market & Counterparty Risk, BNP Paribas

Behnaz Zargar: Senior Quantitative Analyst, Market & Counterparty Risk, BNP Paribas

Behnaz Zargari is a Quant Risk in BNP Paribas. She has been working in the banking industry for ten years, mainly on counterparty risk computation for OTC products and listed derivatives (including Wrong-Way Risk modelling, Initial Margin computation, pricing of exotic derivatives) as well as market risk modelling (including volatility models for VaR computation, correlation models for IRC, CRM and DRC).

Her research contributions are around the modelling of “information” in financial markets (via the theory of the enlargement of filtrations), and the modelling of “dependence” for credit derivatives (via the notion of Markov chain copula).

Behnaz holds a B.Sc. and an M.Sc. in mathematics from Sharif University of Technology (Iran), and a Ph.D. in mathematics from University of Evry (France).

15.00 – 15.20: Afternoon Break and Networking Opportunities

All Streams

15.20 – 16.00: Closing Presentation:

Machine Learning in Finance

Paul Bilokon:

CEO, Thalesians, Visiting Professor, Imperial College

Paul Bilokon: CEO, Thalesians, Visiting Professor, Imperial College

Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

08.30 – 09.00: Morning Welcome Coffee

Chair: Machine Learning

Arun Verma:

Head of Quantitative Research Solutions, Bloomberg, LP

Arun Verma: Head of Quantitative Research Solutions, Bloomberg, LP

Dr. Arun Verma joined the Bloomberg Quantitative Research group in 2003. Prior to that, he earned his Ph.D from Cornell University in the areas of computer science & applied mathematics. At Bloomberg, Mr. Verma’s work initially focused on Stochastic Volatility Models for Derivatives & Exotics pricing and hedging. More recently, he has enjoyed working at the intersection of diverse areas such as data science (for structured & unstructured data), innovative quantitative & machine learning methods and finally interactive visualizations to help reveal embedded signals in financial data.

Machine Learning Stream

09.00 – 09.45: “Explainability of Learning Models”

Harsh Prasad:

Vice President, Morgan Stanley

Harsh Prasad: Vice President, Morgan Stanley 

Harsh currently works with Morgan Stanley in Quant Analytics Group. He started his career as a programmer focussed on developing data driven algos in the areas of speech recognition, image processing and bioinformatics. He then moved to financial risk management and over the last 12 years has worked in various roles through the life cycle of models. In these roles, he has been continuously enthusiastic to applying machine learning in problems related to behavioural assumptions, data quality, recommender systems,  model benchmarking and text analytics. His current role requires him reviewing all Machine Learning models used by the firm and providing direction to shaping AIML governance framework and strategy. He is also a visiting lecturer with universities and training institutions.

Machine Learning Stream

09.45 – 10.30: Portfolio Insights from Machine Learning

Abstract: Machine learning and natural language processing has a wide array of applications for institutional investors. Previous research has shown financial news as a reliable resource for risk management, asset allocation, alpha generation and many other practices. In this presentation we will explore applying similar machine learning techniques to earnings call transcripts to provide investors with an alternative data source uncorrelated with traditional risk and return factors.

 

Christopher Kantos:

Managing Director and Head of Quantitative Research, Alexandria Technology

Christopher Kantos: Managing Director and Head of Quantitative Research, Alexandria Technology

Mr. Christopher Kantos is a Managing Director and Head of Quantitative Research at Alexandria Technology. In this role, he focuses on maintaining and growing new business in EMEA, and exploring ways in which natural language processing and machine learning can be applied in the financial domain. Prior, he spent 15 years working in financial risk at Northfield Information Services as a Director and Senior Equity Risk Analyst. Mr. Kantos earned a BS in computer engineering from Tufts University.

10.30 – 11.00: Morning Break and Networking Opportunities

Machine Learning Stream

11.00 – 11.45: Application of Federated Learning in Finance and Insurance

Malgorzata Smietanka

Malgorzata Smietanka

Malgorzata Smietanka is a qualified actuary with industry experience in insurance and finance, risk management. She is also a Researcher at UCL, Computer Science. Her current research is focussed on Federated Learning and privacy preserving machine learning within insurance.

Machine Learning Stream

11.45 – 12.30: “Multiresolution Learning of Market Objects”

Ioana Boier:

Ioana Boier:

I have a Ph.D. in Computer Science from Purdue University. In addition, I have completed graduate coursework in Financial Mathematics at NYU and Big Data at Harvard University. Prior to joining Citadel, I was a Director in the Global Markets Division at BNP Paribas where I managed the Interest Rate Options & Inflation quantitative research team. Before transitioning into Finance, I was a research staff member at the IBM T. J. Watson Research Center.

12.30 – 13.30: Lunch

Machine Learning Stream

13.30 – 14.15: “Leveraging Large Language Models to extract ESG information in practice”

Robert Dargavel Smith:

Lead Data Scientist, Clarity AI

Robert Dargavel Smith: Lead Data Scientist, Clarity AI

“Robert Smith is a Lead Data Scientist at Clarity AI. Previously he was Head of Data Science at IHS Markit (now part of S&P Global). He has worked in capital markets for over 25 years in Banco Santander and ABN Amro, holding various positions from Head of CVA Desk to Global Head of Quantitative Analysis.”

Machine Learning Stream

14.15 – 15.00: “Using Machine Learning for Calibration & Pricing of Financial Instruments”

Arun Verma:

Head of Quantitative Research Solutions, Bloomberg, LP

Arun Verma: Head of Quantitative Research Solutions, Bloomberg, LP

Dr. Arun Verma joined the Bloomberg Quantitative Research group in 2003. Prior to that, he earned his Ph.D from Cornell University in the areas of computer science & applied mathematics. At Bloomberg, Mr. Verma’s work initially focused on Stochastic Volatility Models for Derivatives & Exotics pricing and hedging. More recently, he has enjoyed working at the intersection of diverse areas such as data science (for structured & unstructured data), innovative quantitative & machine learning methods and finally interactive visualizations to help reveal embedded signals in financial data.

15.00 – 15.20: Afternoon Break and Networking Opportunities

All Streams

15.20 – 16.00: Closing Presentation:

Machine Learning in Finance

Paul Bilokon:

CEO, Thalesians, Visiting Professor, Imperial College

Paul Bilokon: CEO, Thalesians, Visiting Professor, Imperial College

Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

08.30 – 09.00: Morning Welcome Coffee

Chair: Alt Data, Crypto & DeFi

Anmar Al-Wakil:

Senior Data Scientist, RavenPack

Anmar Al-Wakil: Senior Data Scientist, RavenPack

Anmar is a senior data scientist at RavenPack. Before joining RavenPack in 2021, he worked as a quantitative researcher at Natixis Investment Managers for nearly 8 years, where he developed systematic investment strategies within the technology platform. At RavenPack, Anmar excavates cutting-edge insights from news sentiment to elaborate alpha-generating strategies across equity, credit, and derivatives instruments. In addition, he advices some of the world’s top hedge funds and asset managers on the use of NLP-driven analytics in finance.

He holds a PhD in Quantitative Finance from the University of Paris Dauphine-PSL along with a Master’s degree in Mathematical Finance. Anmar has written articles in portfolio selection and machine learning that were presented in multiple conferences. His article about asset pricing won the Best Doctoral Paper of the Multinational Finance Society. He is also a part-time Associate Professor at the University of Paris-Est where he heads the MSc in Portfolio Management.

Alt Data, Crypto & DeFi Stream

09.00 – 09.45: AI, Alt Data and Macroeconomic Forecasting

Alexander Denev:

Co – Founder, TurnLeaf Analytics | Lecturer in AI, University of Oxford

Alexander Denev: Co – Founder, TurnLeaf Analytics | Lecturer in AI, University of Oxford

Alexander has more than 15 years of experience in finance, financial modelling and machine learning and was previously Head of AI – Financial Services Advisory in Deloitte. Prior to joining Deloitte, he led the Quantitative Research & Advanced Analytics at IHS Markit where he created and maintained a center of excellence.

He has written several papers and two books on topics ranging from stress testing and scenario analysis to asset allocation. He has provided thought leadership engagements for conferences, journals and global forums. He also worked as a senior advisor to Risk Dynamics, an arm of McKinsey & Company. Previously he was Director of Risk Models at the Royal Bank of Scotland, where his responsibilities included development of the stress testing methodologies and credit models, and a Fixed Income Structurer for a front office desk. He has also held roles at the European Investment Bank and the European Investment Fund and has participated in the engineering of both the European Financial Stability Facility and the European Stability Mechanism.

Alexander Denev attained his Master of Science degree in Physics with a focus on Artificial Intelligence from the University of Rome, Italy, and he holds a degree in Mathematical Finance from the University of Oxford, UK, where he continues as a visiting lecturer.

Alt Data, Crypto & DeFi Stream

09.45 – 10.30: The Role of Media Sentiment in Factor Investing: Added Value of Alternative Data

Svetlana Borovkova:

Head of Quantitative Modelling, Probability & Partners. Associate Prof, Vrije Universiteit Amsterdam

Svetlana Borovkova: Head of Quantitative Modelling, Probability & Partners and Associate Professor, Vrije Universiteit Amsterdam

Currently Head of Quantitative Modelling at Probability & Partners and Associate Professor at Vrije University Amsterdam, Dr Svetlana Borovkova has specialized in applying mathematical and statistical methods to problems within quantitative finance and risk management.

Dr Borovkova’s research extends in many areas, such as news analytics for finance, derivatives pricing, commodity markets and risk management in the face of new regulation. She is also a consultant for the Dutch Central Bank and the founder and principal consultant of DataDecisions: Financial Risk Consultancy.

Dr Borovkova is a frequent speaker on international conferences, such as Global Derivatives, Risk Minds, Bachelier Congress for Mathematical Finance, Sentiment Analysis and Behavioural Finance and others.

Previously she held an assistant professor position in Delft University of Technology and a trading analyst position in Shell Trading, London.

She got her PhD in 1998 from the University of Groningen, The Netherlands, and Oregon State University, USA and MSc degree in applied mathematics and computer science from Moscow and Utrecht.

10.30 – 11.00: Morning Break and Networking Opportunities

Alt Data, Crypto & DeFi Stream

11.00 – 11.45: “Uniswap V3 and beyond – Modelling Automated Market Makers (AMMs)”

Katia Babbar:

University of Oxford, Academic Visitor & Immersive Finance, co-Founder

Katia Babbar: University of Oxford, Academic Visitor & Immersive Finance, co-Founder

Alt Data, Crypto & DeFi Stream

11.45 – 12.30: Derivatives on Crypto Assets in Decentralized Finance (DeFi)

  • Liquid crypto derivatives in DeFi
  • Comparison with traditional finance: pricing by replication vs supply/demand pricing
  • Examples of protocols and payoffs: perpetual swaps and futures, vanilla and inverse options, perpetual power futures, forward-starting straddles
  • Quantitative approach for arbitrage-free valuation and replication of crypto derivatives

Artur Sepp:

Head Systematic Solutions and Portfolio Construction, Sygnum Bank

Artur Sepp: Head Systematic Solutions and Portfolio Construction, Sygnum Bank

Artur Sepp is Head Systematic Solutions and Portfolio Construction at Sygnum Bank’s Asset Management in Zurich, specializing in crypto assets and decentralized finance. Prior, Artur led quantitative research at a systematic hedge fund (Quantica Capital) focusing on data-driven investment strategies and asset allocation in global managed futures. In previous roles, Artur worked as front office Quant Strategist on the implementation of systematic solutions in private banking (Julius Baer), and on the full-cycle development of quantitative solutions and derivatives in investment banking (Merrill Lynch/BofA).

Artur is dedicated to connecting financial applications with science and technology. His expertise covers quantitative investing and asset allocation, modeling of financial markets and instruments, statistical and Machine Learning methods, modern computational and programming tools. His 14 years professional experience includes performing in leading roles at top quant teams in New-York, London, and Zurich.

Artur has a PhD in Mathematical Statistics from University of Tartu, an MSc in Industrial Engineering and Management Sciences from Northwestern University, and a BA cum laude in Mathematical Economics from Tallinn University of Technology. He is the author and co-author of several research articles on quantitative finance published in key journals. Artur is known for contributions to stochastic volatility and credit risk modelling with an H-index of 16. He is a member of the editorial board of the Journal of Computational Finance. Artur loves martial arts, water, and mountain sports.

12.30 – 13.30: Lunch

Alt Data, Crypto & DeFi Stream

13.30 – 14.15: Modeling Implied Volatility Surfaces of Crypto Options

Parviz Rakhmonov:

Vice President, Quantitative Analyst, Citibank

Parviz Rakhmonov: Vice President, Quantitative Analyst, Citibank

Alt Data, Crypto & DeFi Stream

14.15 – 15.00: Alt Data / Crypto & DeFi Panel

  • Is Crypto the wild west or is it here to stay? What are your thoughts and experience? Tell us how you entered Crypto.
  • Many DeFi protocols are developed by engineers with a computer science background or maybe trading. Is there a room for improvement in terms of design of protocols or risk assessment of their workings by more rigorous mathematical finance applications? That is – would Quants add value?
  • Should there be more close collaboration between computer scientists and mathematical finance to address the many challenges?
  • Crypto is data heaven – most of the data in TradFi resides behind closed doors of institutions. Data is expensive. In crypto, it is very open and data is affordable (especially for academia). What can you see in the data? What projects would you suggest to researchers in the field?
  • What do you think is the role of regulators in the field – and how can Quants help.

Moderator:

Katia Babbar:

University of Oxford, Academic Visitor & Immersive Finance, co-Founder

Katia Babbar: University of Oxford, Academic Visitor & Immersive Finance, co-Founder

Svetlana Borovkova:

Head of Quantitative Modelling, Probability & Partners. Associate Prof, Vrije Universiteit Amsterdam

Svetlana Borovkova: Head of Quantitative Modelling, Probability & Partners and Associate Professor, Vrije Universiteit Amsterdam

Currently Head of Quantitative Modelling at Probability & Partners and Associate Professor at Vrije University Amsterdam, Dr Svetlana Borovkova has specialized in applying mathematical and statistical methods to problems within quantitative finance and risk management.

Dr Borovkova’s research extends in many areas, such as news analytics for finance, derivatives pricing, commodity markets and risk management in the face of new regulation. She is also a consultant for the Dutch Central Bank and the founder and principal consultant of DataDecisions: Financial Risk Consultancy.

Dr Borovkova is a frequent speaker on international conferences, such as Global Derivatives, Risk Minds, Bachelier Congress for Mathematical Finance, Sentiment Analysis and Behavioural Finance and others.

Previously she held an assistant professor position in Delft University of Technology and a trading analyst position in Shell Trading, London.

She got her PhD in 1998 from the University of Groningen, The Netherlands, and Oregon State University, USA and MSc degree in applied mathematics and computer science from Moscow and Utrecht.

Artur Sepp:

Head Systematic Solutions and Portfolio Construction, Sygnum Bank

Artur Sepp: Head Systematic Solutions and Portfolio Construction, Sygnum Bank

Artur Sepp is Head Systematic Solutions and Portfolio Construction at Sygnum Bank’s Asset Management in Zurich, specializing in crypto assets and decentralized finance. Prior, Artur led quantitative research at a systematic hedge fund (Quantica Capital) focusing on data-driven investment strategies and asset allocation in global managed futures. In previous roles, Artur worked as front office Quant Strategist on the implementation of systematic solutions in private banking (Julius Baer), and on the full-cycle development of quantitative solutions and derivatives in investment banking (Merrill Lynch/BofA).

Artur is dedicated to connecting financial applications with science and technology. His expertise covers quantitative investing and asset allocation, modeling of financial markets and instruments, statistical and Machine Learning methods, modern computational and programming tools. His 14 years professional experience includes performing in leading roles at top quant teams in New-York, London, and Zurich.

Artur has a PhD in Mathematical Statistics from University of Tartu, an MSc in Industrial Engineering and Management Sciences from Northwestern University, and a BA cum laude in Mathematical Economics from Tallinn University of Technology. He is the author and co-author of several research articles on quantitative finance published in key journals. Artur is known for contributions to stochastic volatility and credit risk modelling with an H-index of 16. He is a member of the editorial board of the Journal of Computational Finance. Artur loves martial arts, water, and mountain sports.

Parviz Rakhmonov:

Vice President, Quantitative Analyst, Citibank

Parviz Rakhmonov: Vice President, Quantitative Analyst, Citibank

Anmar Al-Wakil:

Senior Data Scientist, RavenPack

Anmar Al-Wakil: Senior Data Scientist, RavenPack

Anmar is a senior data scientist at RavenPack. Before joining RavenPack in 2021, he worked as a quantitative researcher at Natixis Investment Managers for nearly 8 years, where he developed systematic investment strategies within the technology platform. At RavenPack, Anmar excavates cutting-edge insights from news sentiment to elaborate alpha-generating strategies across equity, credit, and derivatives instruments. In addition, he advices some of the world’s top hedge funds and asset managers on the use of NLP-driven analytics in finance.

He holds a PhD in Quantitative Finance from the University of Paris Dauphine-PSL along with a Master’s degree in Mathematical Finance. Anmar has written articles in portfolio selection and machine learning that were presented in multiple conferences. His article about asset pricing won the Best Doctoral Paper of the Multinational Finance Society. He is also a part-time Associate Professor at the University of Paris-Est where he heads the MSc in Portfolio Management.

15.00 – 15.20: Afternoon Break and Networking Opportunities

15.20 – 16.00: Closing Presentation:

Machine Learning in Finance

Paul Bilokon:

CEO, Thalesians, Visiting Professor, Imperial College

Paul Bilokon: CEO, Thalesians, Visiting Professor, Imperial College

Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

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