Emmanuel Bacry (Ecole Polytechnique and CNRS )
Emmanuel Bacry is the Head of Health/Data Projects at Ecole Polytechnique , the Chief Scientfic officer at the Health Data Hub, and a Senior Research Fellow at CNRS.
His research interests include point processes, large dimensions, big data, statistical signal processing, machine learning, multifractals, statistical finance, and high-frequency finance modeling.
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.
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.
Charles-Albert Lehalle (Capital Fund Management)
Charles-Albert Lehalle is Senior Research Advisor at Capital Fund Management (CFM) and a member of the CFM-Imperial Institute of Quantitative Finance. He was formerly Global Head of Quantitative Research at Crédit Agricole Cheuvreux, and Head of Quantitative Research on Market Microstructure in the Equity Brokerage and Derivative Department of Crédit Agricole Corporate Investment Bank.
He holds a Ph.D. in applied mathematics and has published many academic papers about the use of stochastic control and stochastic algorithms for optimizing trading flows. He has also authored papers on post-trade analysis, market impact estimation and modelling the dynamics of limit order books. He is co-author of “Market Microstructure in Practice” and has provided research and expertise on this topic to investors, intermediaries and policy-makers such as the European Commission, the French Senate and the UK Foresight Committee. He belongs to the ESMA Consultative Workgroup on Financial Innovation and Scientific Committee of the French market regulator (AMF).
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.
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.