Speakers

Hans Buehler is Managing Director at JP Morgan, where he leads the “Analytics, Automation and Optimization” program in Equities and runs the Equities and Investor Services Data Analytics Quantitative Research team. His mandate is data-driven business transformation across derivatives, cash equity, electronic trading, prime, and securities services using both modern machine learning and classic derivatives analytics, AI-driven electronic execution and derivative risk management, and the use of modern machine learning techniques for engaging with clients. His team is behind JP Morgan’s LOXM AI effort in electronic trading. Before joining JP Morgan in 2008, Hans worked for seven years at Deutsche Bank. He has a PhD in Mathematical Finance from Technical University (TU) Berlin and a Masters degree in Stochastic analysis from Humboldt University Berlin.

Rama Cont is Professor of Mathematical Finance at the University of Oxford and head of the Oxford Mathematical & Computational Finance group. His previous appointments include faculty positions at Imperial College London, Columbia University (New York), Ecole Polytechnique (France) and Universite de Paris VI. His research focuses on stochastic processes, mathematical modelling in finance, systemic risk modeling and foundations and applications of data science. Prof. Cont received the Louis Bachelier Prize by the French Academy of Sciences in 2010 and the Royal Society Award for Excellence in Interdisciplinary Research in 2017 for his research on systemic risk modelling. He was elected Fellow of the Society for Industrial and Applied Mathematics (SIAM) in 2017 for his 'contributions to stochastic analysis and mathematical modelling in finance'.

Kay Giesecke is Professor of Management Science & Engineering at Stanford University. He is the Director of the Advanced Financial Technologies Laboratory and the Director of the Mathematical and Computational Finance Program.

Kay's research is driven by important applications in areas such as credit risk management, investment management, and, most recently, housing finance. Kay has won the JP Morgan AI Faculty Research Award (2019), the SIAM Financial Mathematics and Engineering Conference Paper Prize (2014), the Fama/DFA Prize for the Best Asset Pricing Paper in the Journal of Financial Economics (2011),and the Gauss Prize of the Society for Actuarial and Financial Mathematics of Germany (2003). Kay is the recipient of the Management Science & Engineering Graduate Teaching Award (2007), a DFG Postdoctoral Fellowship (2002-03), and a Deutsche Bundesbank Fellowship (2002).

Igor Halperin is Research Professor of Financial Machine Learning at NYU Tandon School of Engineering. His research focuses on using methods of Reinforcement Learning, Information Theory, neuroscience and physics for financial problems such as portfolio optimization, dynamic risk management, and inference of sequential decision-making processes of financial agents. Prior to joining NYU Tandon, Igor was an Executive Director of Quantitative Research at JPMorgan, and before that he worked as a quantitative researcher at Bloomberg LP. Igor has published numerous articles in finance and physics journals, and is a frequent speaker at financial conferences. He has a Ph.D. in theoretical high energy physics from Tel Aviv University, and a M.Sc. in nuclear physics from St. Petersburg State Technical University. He serves as advisor to several fintech and data science start-ups and risk management firms.

Matthew Hodgson is founder and CEO of Mosaic SmartData. Matthew was a Yen trader at Salomon Brothers (now Citi), then a rates salesman at Deutsche Bank, and went on to become MD, Global Head of and responsible for FICC Data Strategy and served as Deutsche Banks iSwap board member from 2011 to 2014. , a position he held until April 2014 he was MD at Deutsche Bank responsible for FICC Data Strategy and served as Deutsche Banks iSwap board member from 2011 to 2014.

Marcos López de Prado has over 20 years of experience developing investment strategies with the help of machine learning algorithms and supercomputers. He recently sold his patents to AQR Capital Management, where he was a principal and AQR’s first head of machine learning. Marcos founded and led Guggenheim Partners’ Quantitative Investment Strategies business, where he managed up to $13 billion in assets. He has published dozens of scientific articles on machine learning and supercomputing in the leading academic journals, and SSRN ranks him as the most-read author in economics. Marcos is the author of Advances in Financial Machine Learning (Wiley, 2018). He earned a PhD in financial economics (2003), a PhD in mathematical finance (2011) from Universidad Complutense de Madrid and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard and Cornell, where he teaches a financial machine learning course at the School of Engineering. In 2019, Marcos received the ‘Quant of the Year Award’ from The Journal of Portfolio Management.

David Siska is Lecturer in Mathematics at the University of Edinburgh and Quantitative analyst at VegaProtocol. David’s expertise and research areas include mathematical finance, theoretical, computational and applied aspects of stochastic processes and partial differential equations. Prior to joining the University of Edinburgh, he was a quantitative analyst at BNP Paribas in the Fixed Income Research and Strategies Team where he developed and maintained pricing and risk models for government bonds, interest rate swaps, constant maturity swaps, corporate bonds and credit default swaps.

Ben Wood is Executive Director and EMEA Head of the Equity Derivatives Modelling team at JPMorgan. The team builds valuation and risk management models for the equity derivatives business, applying classical mathematical finance techniques and modern Machine Learning methods. Before joining JPMorgan in 2011, Ben worked as an equity derivatives quant at Deutsche Bank. Ben holds an MPhys from Oxford University and a PhD from Imperial College London; his PhD research focused on quantum Monte Carlo simulations.

Dominic Wright is a Director at Credit Suisse, based in London, where he works in Quantitative Strategies Credit. He focusses on the modelling of flow credit derivatives. He is a graduate of Cambridge University and hold a PhD in Mathematics from Imperial College London.