Hanna Assayag (HSBC)
Hanna is a Managing Director in HSBC Global Markets FX and the Head of the eRisk Quant team for STIRs & EM Forwards.
She joined HSBC in March 2010 and has worked on the FX Spot, FX Swaps, Non-Deliverable Forwards and Cash Bonds electronic market-making desks. She also created the new Quantitative Analytics Internship program for HSBC Global Markets and leads a variety of multi-assets digital initiatives.
Hanna holds a Paris Grande Ecole degree from Telecom ParisTech, and a DEA (El Karoui) in Probabilities and Finance from Université Pierre and Marie Curie Paris.
Tomaso Aste (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, artificial intelligence and machine learning. He is passionate in the investigation of the effect of technologies on socio-economic systems and currently he focuses on machine learning and probabilistic modelling. He is co-founder and Scientific Director of the UCL Centre for Blockchain Technologies, founder and Head of the Financial Computing and Analytics Group at UCL, Member of the Board of the ESRC LSE-UCL Systemic Risk Centre and Member of the Board of the Whitechapel Think Thank. Prof. Aste is leading research on Blockchain Technologies for financial regulation (BARAC), on the impact Blockchain Technologies on privacy and business models and on financial technologies for regulation and supervision (FinTech). He collaborates with the Financial Conduct Authority, The Bank of England and HMRC. He contributes the All-Party Parliamentary Group on FinTech. He is leading an initiative for training to FinTech central bankers and regulators across South America. He is advisor and consultant for several financial companies, banks, FinTech firms and digital-economy start-ups.
Alexander Barzykin (HSBC)
Alexander joined HSBC FX eRisk as director in 2015 and is currently leading design and development of spot FX execution algorithms. He was previously building algorithmic execution suite at RBS, in equities and fixed income.
Alexander moved to finance in 2007 from the field of theoretical chemical physics. He received his PhD in physics and mathematics from the Moscow Institute for Physics and Technology in 1989 and held senior research positions at the Institute of Chemical Physics, Russian Academy of Sciences, and the National Institute of Advanced Industrial Science and Technology, Japan.
Pierre Collin-Dufresne is a Professor at the Swiss Finance Institute of the École Polytechnique Fédérale de Lausanne. He has published in leading academic journals such as Econometrica, The American Economic Review, and The Journal of Finance and won various research awards including Amundi Smith Breeden Prizes. He has served as director of the American Finance Association, director of the Western Finance Association, associate editor for several leading finance journals, and has been a member of the Center of Economic Policy Research and of the National Bureau of Economic Research. Before joining the SFI, he was the Carson Family Professor of Business at Columbia University. He previously held professorships at the Haas School of Business of UC Berkeley and at Carnegie Mellon University. Professor Collin-Dufresne also worked in the Quantitative Strategies group of Goldman Sachs Asset Management and as consultant for the Federal Reserve Bank of New York and the European Central Bank, as well as for Cornerstone Research.
Albina Danilova (London School of Economics)
Albina Danilova has joined the Department of Mathematics at LSE in 2009 and is currently an Associate Professor. She was awarded a Ph.D. by the Department of Operations Research and Financial Engineering at Princeton University. Before joining the LSE Department of Mathematics in 2009, she held a postdoctorate position at the Department of Mathematics at Carnegie Mellon University and a Nomura Junior Research Fellowship at the Mathematical Institute, University of Oxford.
Her research interests span asymmetric information, derivative pricing, stochastic calculus, insider trading, stochastic control, and equilibrium theory.
Olivier Guéant (Université Paris 1 Panthéon-Sorbonne)
Olivier Guéant is a Professor of Applied Mathematics at Université Paris 1 and a member of CES (Centre d'Economie de la Sorbonne). Before joining Université Paris , he was Professor at ENSAE ParisTech, in charge of the Quantitative Finance track of the school (2015-2016), Associate Professor (Maître de Conférences) of Applied Mathematics at Université Paris-Diderot (2010-2015), Lecturer at Sciences Po Paris in the Master Finance and Strategy (2010-2013), Lecturer at Sciences Po Paris in Microeconomics and Macroeconomics (2009-2010), Teaching Fellow at Harvard University (2008).
His research interests are in quantitative finance, optimal control and games in addition to envionmental economics and real-time bidding.
Nikolaus Hautsch (University of Vienna)
Nikolaus Hautsch is Professor of Finance and Statistics at the University of Vienna. He earned his Ph.D. in Econometrics in 2003 from the University of Konstanz. From 2004 to 2007 he joined the Department of Economics of the University of Copenhagen as Assistant Professor and Associate Professor. Until 2013 he held the Chair of Econometrics at Humboldt University Berlin and was director of the Berlin Doctoral Program in Economics and Management Science.
He is elected fellow of the Society for Financial Econometrics, research fellow of the Center for Financial Studies (CFS) Frankfurt and a staff member of the Vienna Graduate School of Finance. Nikolaus held visiting positions at the University of Technology, Sydney, the University of Melbourne, the Université Catholique de Louvain, the University of Cambridge and Duke University.
His research focuses on the econometrics of high-frequency financial data, market microstructure analysis, the modelling of volatility and liquidity, systemic risk and information processing on financial markets. He publishes in leading journals in the area of finance, econometrics and statistics. Currently, he serves as associate editor of the Journal of Business and Economic Statistics, the Journal of Applied Econometrics, the Journal of Financial Econometrics and the International Journal of Forecasting, among others.
Sergey Nadtochiy (Illinois Institute of Technology)
Sergey Nadtochiy is an Associate Professor of Applied Mathematics at the Illinois Institute of Technology. He earned his PhD from Princeton University. His research interest in financial mathematics focuses on market microstructure and liquidity risk, derivatives markets and optimal investments.
Before joining the Illinois Institute of Technology he was a senior postdoctoral Research fellow in University of Oxford and then Assistant Professor at the University of Michigan.
Roel Oomen (Deutsche Bank)
Roel is the head of FIC quantitative trading at Deutsche Bank. He started his industry career as a quant in cash equity algo trading in 2006, and subsequently held various roles in electronic FX spot trading, including co-head of the business.
Roel holds a PhD in econometrics, is a senior research fellow at the London School of Economics, and has published widely on the econometric analysis of high frequency data and FX trading.
Emiliano Pagnotta (Imperial College London)
Emiliano Pagnotta is an Assistant Professor of Finance at Imperial College Business School. His research focuses on the exchange and valuation of financial assets and the organization and evolution of the markets where those assets trade. Recent work by Emiliano analyzes the consequences of speed and fragmentation in financial markets, the identification of private information in stock and derivatives markets, and the valuation of Bitcoin and other blockchain assets. His research is regularly presented in leading academic and professional conferences and published in academic journals such as Econometrica and The Review of Financial Studies.
Before joining Imperial College, Emiliano Pagnotta was at the New York University Stern School of Business. He holds a BA in Economics from the University of Buenos Aires, an MA in Economics from Universidad de San Andrés, Argentina, and a Ph.D. in Economics from Northwestern University.
Mikko Pakkanen (Imperial College London)
Mikko Pakkanen is a Senior Lecturer in Mathematical Finance and Statistics at Imperial College London. He is also Co-Director of the EPSRC Centre of Doctoral Training in Financial Computing & Analytics and Co-Director of the MSc in Mathematics and Finance at Imperial College London, where he currently teaches deep learning for finance and quantitative risk management.
Mikko’s research is interdisciplinary and lies in the intersection of data science and quantitative finance. His specific research interests include statistical modelling of high frequency financial data and volatility, while recently extending also to deep learning in statistical and computational finance. Mikko obtained his PhD in Applied Mathematics from the University of Helsinki in 2010. Prior to joining Imperial, he was a Postdoctoral Research Fellow at the Center for Research in Econometric Analysis of Time Series (CREATES) at Aarhus University.
Paris Pennesi (HSBC)
Paris is Head of Quant Strategies for Spot FX & Commodities for the electronic trading team, eRisk, at HSBC. Previously, he has worked at MAN AHL, JP Morgan and RBS, creating systematic investments strategies and algorithms for market making and execution across FX, Equities and Futures markets. He is also Honorary Associate Professor at UCL where he teaches Market Microstructure and had academic positions at London School of Economics and Cambridge University. He holds a PhD in Artificial Intelligence and a Master Degree in Electronic Engineering.
Mathieu Rosenbaum (Ecole Polytechnique)
Mathieu Rosenbaum is full professor at Ecole Polytechnique since 2016, where he holds the chair "Analytics and Models for Regulation". He obtained his Ph.D from University Paris-Est in 2007. After being Assistant Professor at École Polytechnique, he became Professor at University Pierre et Marie Curie (Paris 6) in 2011.
Mathieu's research mainly focuses on statistical finance issues such as volatility modeling, analysing market microstructure or designing statistical procedures for high frequency data. In particular, he is one of the organizers of the conference "Market Microstructure, Confronting Many Viewpoints", which takes place every two years in Paris. Mathieu has collaborations with various financial institutions, notably BNP-Paribas since 2004. He is one of the editors in chief of "Market Microstructure and Liquidity" , a managing editor for "Quantitative Finance" and associate editor for "Electronic Journal of Statistics", "Journal of Applied Probability", "Mathematical Finance"; "Mathematics and Financial Economics", "Statistical Inference for Stochastic Processes", "SIAM Journal in Financial Mathematics"," Springer Briefs" and "Statistics and Risk Modeling". He received the Europlace Award for Best Young Researcher in Finance in 2014 and the European Research Council Grant in 2015.
Pamela Saliba (Pictet Asset Management)
Pamela Saliba is part of the Quantitative Equities Team at Pictet Asset Management in Geneva. She received her Master in Probability and Finance (El Karoui master) from University Paris-VI, and earned her PhD in Applied Mathematics from Ecole Polytechnique in 2019. During her PhD, she worked in the Surveillance department at the French regulator AMF. Her PhD is in the area of market microstructure, with a particular focus on high-frequency data analysis, market impact and order book modelling.
Kimmo Soramäki (Financial Network Analytics)
Kimmo Soramäki is the founder and CEO of Financial Network Analytics (FNA) and the founding Editor-in-Chief of the Journal of Network Theory in Finance. Kimmo started his career as an economist at the Bank of Finland where in 1997, he developed the first simulation model for interbank payment systems. In 2004, while at the research department of the Federal Reserve Bank of New York, he was among the first to apply methods from network theory to improve our understanding of financial interconnectedness. During the financial crisis of 2007-2008, Kimmo advised several central banks, including the Bank of England and European Central Bank, in modeling interconnections and systemic risk. This work led him to found FNA in 2013 to solve important issues around financial risk and for exploring the complex financial networks that play a continually larger role in the world around us. Kimmo holds a Doctor of Science in Operations Research and a Master of Science in Economics (Finance), both from Aalto University in Helsinki.
James Tromans (Google)
James is a Technical Director within the Office of the CTO at Google Cloud. James specialises in data strategy, HPC and machine learning. Prior to joining Google, James worked at Citi, most recently as head of data science for the FX trading business. Before Citi, James was a Fintech co-founder, having previously worked as an engineer across different industries. James holds a DPhil in the Computational Neuroscience of Vision from the University of Oxford.
Yajun Wang (Baruch College)
Yajun Wang is an Associate Professor of Finance at the Zicklin School of Business, Baruch College in New York. She earned her PhD in Finance in 2011 from Olin Business School, Washington University in St Louis. From 2011 to 2018 she joined the Robert H. Smith School of Business as an Assistant Professor of Finance.
Her research focuses on theoretical and empirical asset pricing and market microstructure.
Stefan Zohren (University of Oxford)
Stefan Zohren is an Associate Professor at the Machine Learning Research Group at the Oxford-Man Institute for Quantitative Finance (OMI). Before joining the OMI, Stefan worked on equities market making as a quant researcher/trader at two leading HFT firms in London. Prior to that, he coordinated the Quantum Optimisation and Machine Learning project, a joint research project of Oxford University, Nokia Technologies and Lockheed Martin.
His background is in theoretical physics, probability theory and statistics. Stefan’s research interests include statistical physics approaches to machine learning, information theory and optimisation, quantum computing, as well as machine learning applied to finance, particularly market microstructure and high-frequency data.