MD, Head of Quantitative Analytics and Quantitative Development, NatWest Markets
Vladimir Piterbarg: MD, Head of Quantitative Analytics and Quantitative Development at NatWest Markets
Professor and Dept. Chair of FRE Tandon, New York University
Peter Carr: Professor and Dept. Chair of FRE Tandon, New York University
Dr. Peter Carr is the Chair of the Finance and Risk Engineering Department at NYU Tandon School of Engineering. He has headed various quant groups in the financial industry for the last twenty years. He also presently serves as a trustee for the National Museum of Mathematics and WorldQuant University. Prior to joining the financial industry, Dr. Carr was a finance professor for 8 years at Cornell University, after obtaining his Ph.D. from UCLA in 1989. He has over 85 publications in academic and industry-oriented journals and serves as an associate editor for 8 journals related to mathematical finance. He was selected as Quant of the Year by Risk Magazine in 2003 and Financial Engineer of the Year by IAQF/Sungard in 2010. From 2011 to 2014, Dr. Carr was included in Institutional Investor’s Tech 50, an annual listing of the 50 most influential people in financial technology.
In the 2 years Dr. Carr been FRE dept. chair, applications increased from 1,300 per year to 1,900 per year. The number of FRE Masters students in residence was the highest in any 2-year period. For the incoming 2018 class, current verbal GRE is 169/170 and GPA is 3.82. FRE moved up in Quantnet rankings both years. An online summer course was initiated last summer and an on-campus bootcamp will be initiated this summer. Six electives on machine learning in finance were introduced. The distance learning room will become operational this summer.
Reader, Financial Mathematics, Cass Business School
Laura Ballotta: Reader, Financial Mathematics, Cass Business School
Dr Ballotta works in the areas of quantitative finance and risk management. She has written on topics including stochastic modelling for financial valuation and risk management, numerical methods aimed at supporting financial applications, and the interplay between finance and insurance.
Recent major contributions have appeared in Journal of Financial and Quantitative Analysis, European Journal of Operational Research and Quantitative Finance among others.
She serves as associate editor and referee for a number of international journals in the field.
Laura Ballotta obtained her PhD in Mathematical and Computational Methods for Economics and Finance from the Università degli Studi di Bergamo (Italy), following her BSc in Economics from Università Cattolica del Sacro Cuore, Piacenza (Italy), and MSc in Financial Mathematics from the University of Edinburgh – jointly awarded with Heriot-Watt University (UK). Laura has previously held positions at Università Cattolica del Sacro Cuore, Piacenza (Italy), and Department of Actuarial Science and Statistics, City University London (UK).
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.
Associate Director, Quantum Computing, USRA
Davide Venturelli: Associate Director, Quantum Computing, USRA
Davide Venturelli works in the NASA Intelligent System Division (TI) as a research scientist under the NASA Academic Mission Service contract. He is currently Quantum Computing team lead and Science Operations Manager of the Research Institute of Advanced Computer Science (RIACS) at USRA. Venturelli is currently invested in research projects dealing with quantum optimization applications and their near-term implementation in real hardware.
Head of Quantitative Portfolio Solutions, Alphadyne Asset Management
Ioana Boier: Head of Quantitative Portfolio Solutions, Alphadyne Asset Management
Ioana is the Head of Quantitative Portfolio Solutions, Alphadyne Asset Management.
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.
Executive Chairman and Head of Quant Research, CompatibL
Alexander Sokol: Executive Chairman and Head of Quant Research, CompatibL
Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder of Duality Group, where he served as CTO from 2017 to 2020.
Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).
Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.
AI Research Associate, Fidelity Investments
Igor Halperin: AI Research Associate, Fidelity Investments
Igor Halperin is an AI Research Associate at Fidelity Investments. 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. Igor has an extensive industrial and academic experience in statistical and financial modeling, in particular in the areas of option pricing, credit portfolio risk modeling, portfolio optimization, and operational risk modeling. Prior to joining Fidelity, Igor worked as a Research Professor of Financial Machine Learning at NYU Tandon School of Engineering. Before that, Igor was an Executive Director of Quantitative Research at JPMorgan, and 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 co-authored the books “Machine Learning in Finance: From Theory to Practice” (Springer 2020) and “Credit Risk Frontiers” (Bloomberg LP, 2012). Igor 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.
Stuart School of Business, Illinois Institute of Technology
Matthew Dixon: Stuart School of Business, Illinois Institute of Technology
Matthew Dixon, Ph.D, FRM, began his career as a quant in structured credit trading at Lehman Brothers. He has consulted for numerous investment management, trading and financial technology firms in machine learning and risk analytics. He is the author of the 2020 textbook “Machine Learning in Finance: From Theory to Practice” and has written over 20 peer reviewed papers on machine learning and computational finance, including SIAM J. Financial Mathematics and the Journal of Computational Finance. He is the recipient of an Illinois Tech innovation award, and his research has been funded by Intel and the NSF. Matthew has recently contributed to the CFA syllabus on machine learning and he currently serves on the CFA advisory committee for quantitative trading. He has been invited internationally to give talks at prestigious seminars organized by investment banks and universities in addition to being quoted in the Financial Times and Bloomberg Markets. He holds a Ph.D. in Applied Math from Imperial College, has held visiting academic appointments at Stanford and UC Davis, and is a tenure-track Assistant Professor at Illinois Tech.
Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
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.
Lecturer in Financial Mathematics, Queen Mary University of London
Kathrin Glau: Lecturer in Financial Mathematics, Queen Mary University of London
Kathrin Glau currently is a Lecturer in Financial Mathematics at Queen Mary University of London & FELLOW co-founded by Marie Skłodowska Curie at École Polytechnique Fédérale de Lausanne. Between 2011 and 2017 she was Junior Professor at the Technical University of Munich. Prior to this she worked as a postdoctoral university assistant at the chair of Prof. Walter Schachermayer at the University of Vienna. In September 2010 she completed her Ph.D. on the topic of Feynman-Kac representations for option pricing in Lévy models at the chair of Ernst Eberlein.
Her research is driven by the interdisciplinary nature of computational finance and reaches across the borders of finance, stochastic analysis and numerical analysis. At the core of her current research is the design and implementation of complexity reduction techniques for finance. Key to her approach is the decomposition of algorithms in an offline phase, which is a learning step, and a fast and accurate online phase. The methods range from model order reduction of parametric partial differential equations to learning algorithms and are designed to facilitate such diverse tasks as uncertainty quantification and calibration, real-time pricing, real-time risk monitoring, and intra-day stress testing.
Senior Associate, Enterprise Architecture, Federal Reserve Bank of New York
Xue Rui: Senior Associate, Enterprise Architecture, Federal Reserve Bank of New York
Xue graduates from University of Notre Dame with Ph.D in physics and Master in Electric Engineering. She is a Senior Associate in Federal Reserve Bank of New York. Her work focuses on developing and deploying NLP and AI technology in the bank. Prior to joining the Federal Reserve Bank, Xue has been working as Scientist in General Electric Global Research Center. Xue’s research interests focus on artificial intelligence, including natural language processing, image analysis, and computer vision. She holds 20 + peer reviewed publications and 10 + patents.
Professor, Courant Institute of Mathematical Sciences, NYU
Petter Kolm: Director of the Mathematics in Finance Master’s Program and Clinical Professor, Courant Institute of Mathematical Sciences, New York University
Petter Kolm is the Director of the Mathematics in Finance Master’s Program and Clinical Professor at the Courant Institute of Mathematical Sciences, New York University. Previously, Petter worked in the Quantitative Strategies Group at Goldman Sachs Asset Management where his responsibilities included researching and developing new quantitative investment strategies for the group’s hedge fund. Petter has coauthored four books: Financial Modeling of the Equity Market: From CAPM to Cointegration (Wiley, 2006), Trends in Quantitative Finance (CFA Research Institute, 2006), Robust Portfolio Management and Optimization (Wiley, 2007), and Quantitative Equity Investing: Techniques and Strategies (Wiley, 2010). He holds a Ph.D. in Mathematics from Yale, an M.Phil. in Applied Mathematics from the Royal Institute of Technology, and an M.S. in Mathematics from ETH Zurich.
Petter is a member of the editorial boards of the International Journal of Portfolio Analysis and Management (IJPAM), Journal of Financial Data Science (JFDS), Journal of Investment Strategies (JoIS) and Journal of Portfolio Management (JPM). He is an Advisory Board Member of Betterment (one of the largest robo-advisors) and Alternative Data Group (ADG). Petter is also on the Board of Directors of the International Association for Quantitative Finance (IAQF) and Advisory Board Member of Artificial Intelligence Finance Institute (AIFI).
As a consultant and expert witness, Petter has provided his services in areas including alternative data, data science, econometrics, forecasting models, high frequency trading, machine learning, portfolio optimization w/ transaction costs and taxes, quantitative and systematic trading, risk management, robo-advisory and investing, smart beta strategies, transaction costs, and tax-aware investing.
Founder & CEO, Riskfuel
Ryan Ferguson: Founder & CEO, Riskfuel
Ryan is Founder and CEO at Riskfuel, a capital markets focused startup that is developing ultra-fast AI-based valuation technologies.Previously, Ryan was Managing Director and Head of Securitization, Credit Derivatives and XVA at Scotiabank. Prior roles have included credit correlation trading and managing the equity derivatives trading desk. Ryan began his career with positions in risk management and financial engineering. Ryan has a PhD in Physics from Imperial College, and a BASc and MASc in Electrical Engineering from the University of Waterloo.
Head of Structured Credit QA, Barclays Investment Bank
Andrey Chirikhin: Head of Structured Credit QA, Barclays Investment Bank
Andrey was formerly Head of Modelling and Quantitative Analytics for L1 Treasury, part of a USD 25bn privately held investment vehicle LetterOne. Prior to LetterOne, Andrey was MD and Head of CVA and CCR quantitative Analytics at RBS. There he has created and run the front office cross asset CVA quant team. He also restructured and led the risk-side quant team charged with delivering a new Basel III compliant internal CCR methodology. The system utilizing the newly delivered methodology has won the 2013 Internal System of the year Risk award. In his 20 year career in investment banking, Andrey held several leadership and senior quant positions at Goldman Sachs, HSBC and Dresdner Kleinwort. Andrey Chirikhin holds PhD in Theoretical Statistics from Warwick University (UK), MBA from INSDEAD and MSc in Applied Mathematics from Moscow Institute for Physics and Technology (Phystech).
Since 2018 Andrey runs his own company, Quantitative Recipes, that advises on wide rage of XVA, long-term market modelling for risk and quant infrastructure.
Senior Director, Head of Methodology and Analytics, Capital Markets Risk Management, CIBC
Hany Farag: Senior Director, Head of Methodology and Analytics, Capital Markets Risk Management, CIBC
Hany Farag is Senior Director and Head of Risk Methodology and Analytics at CIBC. Prior to his current position he was a partner at Eastmoor Capital Partners, LLP; Managing Director and Head of FX Statistical Arbitrage at CIBC; and Head of Quantitative Research at OANDA Corporation. Prior to his industry positions he was a Postdoctoral Fellow at Caltech and at Rice University. He holds a PhD in Mathematical Analysis from Yale, a MS in Theoretical Physics from Yale, and a BSC in Electronics and Communication Engineering from Ain Shams.
Zineb El Filali Ech-Chafiq:
Quantitative Analyst, Natixis
Zineb El Filali Ech-Chafiq: Quantitative Analyst, Natixis
Senior Knowledge Engineer in Technology Innovation, Federal Reserve Bank of New York
Knarig Arabshian: Senior Associate Knowledge Engineer in Technology Innovation, Federal Reserve Bank of New York
I am a Senior Associate Knowledge Engineer in Technology Strategy & Innovation at theFederal Reserve Bank of New York where I conduct research in semantic web technologies and text analytics for structuring financial data.
Previously, I was an Assistant Professor in the Computer Science Department at Hofstra University in Hempstead, NY and a Member of Technical Staff at Bell Labs in Murray Hill, NJ. I have also taught as an Adjunct Professor at Columbia University twice. I received my PhD in Computer Science from Columbia University in 2008, where I worked in theIRT Lab under the advisment of Henning Schulzrinne.
Darko Matovski: CEO, causaLens
Dr. Darko Matovski is the CEO of causaLens. The company is leading Causal AI research, a way for machines to understand cause & effect, and serves some of the most sophisticated organisations. Darko has also worked for cutting edge hedge funds and research institutions. For example, the National Physical Laboratory in London (where Alan Turing worked) and Man Group in London. Darko has a PhD in Machine Learning and an MBA.
Chief Analyst, Danske Bank
Alexandre Antonov: Chief Analyst, Danske Bank
Alexandre Antonov received his PhD degree from the Landau Institute for Theoretical Physics in 1997. He worked for Numerix during 1998-2017 and recently he has joined Danske Bank as the Chief Analyst in Copenhagen.
His activity is concentrated on modeling and numerical methods for interest rates, cross currency, hybrid, credit and CVA/FVA/MVA. AA is a published author for multiple publications in mathematical finance and a frequent speaker at financial conferences.
He has received a Quant of Year Award of Risk magazine in 2016.
Director of Research, Quantica Capital AG
Artur Sepp: Director of Research, Quantica Capital AG
Artur Sepp is Director of Research at Quantica Capital AG in Zurich focusing on development of systematic data-driven trend-following and asset allocation strategies. Prior, Artur worked at Julius Baer in Zurich as Senior Quant Strategist developing algorithmic solutions and investment strategies for the trading and portfolio advisory. Before, Artur has worked in leading roles 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 since 2006. Artur holds a PhD in Mathematical Statistics from University of Tartu, an MSc in Industrial Engineering and Management Sciences from Northwestern University, and a BA in Mathematical Economics with distinction from Tallinn University of Technology. Artur’s research and expertise are on econometric data analysis, statistical machine learning and computational methods along with designing of research and trading infrastructure and technology with applications for quantitative trading, asset allocation and wealth management. He is the author and co-author of several research articles on quantitative finance published in key journals. Artur is known for his contributions to stochastic volatility and credit risk modelling with an H-index of 15. He is a member of the editorial board of the Journal of Computational Finance.
PhD Student in Quantum Computing, UCL
Nedeen Alsharif: PhD Student in Quantum Computing, UCL
Finciraptor, AcadiaSoft, University of Wuppertal and Cape Town
Jörg Kienitz: Finciraptor, AcadiaSoft, University of Wuppertal and Cape Town
Jörg Kienitz works in Quantitative Finance and Machine Learning at Acadiasoft and the owner of the Finciraptor website (finciraptor.de). He is primarily involved in consulting on the development, implementation and validation of models. Jörg lectures at the University of Wuppertal as an Assistant Professor and is an Adjunct Associate Professor at UCT. He has addressed major conferences including Quant Minds and WBS Quant Conference. Jörg has authored four books “Monte Carlo Object Oriented Frameworks in C++” (with Daniel J. Duffy), “Financial Modelling” (with Daniel Wetterau), “Interest Rate Derivatives Explained I” and “Interest Rate Derivatives Explained II” (with Peter Caspers).
His SSRN author page is https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=744396″
XVA and Capital Quantitative Analyst, UBS
Gordon Lee: XVA and Capital Quantitative Analyst, UBS
Associate Director, Quantitative Analyst, Model Validation, Banco Santander
Ángel Rodríguez-Rozas: Associate Director, Quantitative Analyst, Model Validation, Banco Santander
Ángel Rodríguez Rozas holds a Ph.D. in Computational and Applied Mathematics from the University of Lisbon and an M.Sc. in Artificial Intelligence from the Universitat Rovira i Virgili (URV) and the Polytechnic University of Catalonia (UPC). He has authored more than 20 research articles in international peer-reviewed journals in many different areas, including artificial intelligence, numerical methods for PDEs, high-performance computing, plasma physics, the finite element method, seismic wave propagation, and oil&gas simulation and inversion of petrophysical measurements.
Ángel joined Banco Santander in 2018 where he is working as a Quant Analyst in the Internal Validation team, within the Risk Department. As part of his role, Ángel is responsible for leading the design and development of a numerical library for the internal validation of pricing models, including interest rates, FX, credit, commodities, equity, inflation, and xVA. His research efforts are currently focusing on the finance industry, investigating efficient numerical methods (Quasi- and Monte Carlo methods, Finite Elements) and quantum computing algorithms (digital and analog) for the pricing of financial derivatives.
Professor of Mathematics Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation
Stéphane Crépey: Professor of Mathematics at the Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation (LPSM)
Stéphane Crépey is the Professor of Mathematics at the Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation (LPSM). Formerly professor at the Mathematics Department of University of Evry (France), head of Probability and Mathematical Finance and head of the Engineering and Finance branch (M2IF) of the Paris-Saclay Master Program in Financial Mathematics. His research interests are financial modeling, counterparty and credit risk, numerical finance, as well as related mathematical topics in the fields of backward stochastic differential equations and partial differential equations. He is the author of numerous research papers and two books: “Financial Modeling: A Backward Stochastic Differential Equations Perspective” (S. Crépey, Springer Finance Textbook Series, 2013) and “Counterparty Risk and Funding, a Tale of Two Puzzles” (S. Crépey, T. Bielecki and D. Brigo, Chapman & Hall/CRC Financial Mathematics Series, 2014).
He is an associate editor of SIAM Journal on Financial Mathematics, International Journal of Theoretical and Applied Finance, and a member of the scientific council of the French financial markets authority (AMF). Stéphane Crépey graduated from ENSAE and he holds a PhD in applied mathematics from Ecole Polytechnique and INRIA Sophia Antipolis.
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.
He holds a PhD in nano-physics from Cambridge University.
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.
Head of Automatic Adjoint Differentiation, Matlogica
Dmitri Goloubentsev: Head of Automatic Adjoint Differentiation, Matlogica
Dmitri has 15 years of combined experience in model development working on C++ quant libraries. He worked as a Senior Quant Analyst in interest rate derivatives and played a leading role in delivering XVA solution at a major Canadian bank. Prior to focusing on AAD, he was responsible for construction of SIMM/MVA model. Dmitri earned his degree in Maths and Applied Maths from the Moscow State University.
Head of Fair Value Policy, Intesa Sanpaolo
Marco Bianchetti: Head of Fair Value Policy, Intesa Sanpaolo
Marco Bianchetti joined the Market Risk Management area of Intesa Marco joined the Financial and Market Risk Management area of Intesa Sanpaolo in 2008. His work covers pricing and risk management of financial instruments across all asset classes, with a focus on new products development, model validation, model risk management, interest rate modelling, funding and counterparty risk, fair and prudent valuation, applications of Quasi Monte Carlo in finance. He is in charge of the global Fair Value Policy of Intesa Sanpaolo group since Nov. 2015. Previously he worked for 8 years in the front office Financial Engineering area of Banca Caboto (now Banca IMI), developing pricing models and applications for interest rate and inflation trading desks. He is adjunct professor of Interest Rate Models at University of Bologna since 2015, and a frequent speaker at international conferences and trainings in quantitative finance. He holds a M.Sc. in theoretical nuclear physics and a Ph.D. in theoretical condensed matter physics.
Financial Analyst, Prometeia S.p.a
Pietro Rossi: Financial Analyst, Prometeia S.p.a
Pietro Rossi holds a degree in physics from the University of Parma and a Ph.D. from New York University. He has been actively doing research in the field of high energy physics in the early stage of his career and moved later on to high performance computing. In the last ten years he has been involved with mathematical finance. Currently his research activity splits between the use of Fourier transform Methods in finance and machine learning techniques applied to financial risk management.
Since 2018 he has a teaching appointment with the University of Bologna.
Saeed Amen: Founder: Cuemacro
Saeed has a decade of experience creating and successfully running systematic trading models at Lehman Brothers and Nomura. He is the founder of Cuemacro, Cuemacro is a company focused on understanding macro markets from a quantitative perspective. He is the author of ‘Trading Thalesians – What the ancient world can teach us about trading today’ (Palgrave Macmillan), and graduated with a first class honours master’s degree from Imperial College in Mathematics& Computer Science.
David Jessop: Head of Investment Risk at Columbia Threadneedle Investments EMEA APAC
David is the Head of Investment Risk at Columbia Threadneedle Investments EMEA APAC. Previously the Global Head of Equities Quantitative Research at UBS. His areas of research include portfolio analysis and construction, style analysis and risk modelling. He also helps clients understand, use and implement the quantitative tools available from UBS. David joined UBS in 2002. Prior to this, he spent seven years at Citigroup as Head of Global Quantitative Marketing. Before moving to the sell side he spent six years at Morgan Grenfell Asset Management, where he managed index funds, asset allocation funds and also an option overwriting fund.
David graduated from Trinity College, Cambridge with an MA in Mathematics.
Quantitative Analyst, Deutsche Bank
Colin Turfus: Quantitative Analyst, Deutsche Bank
Colin Turfus has worked for the last twelve years as a financial engineer, mainly analysing model risk for credit derivatives and hybrids. More recently his interest has been in the application of perturbation methods to risk management, finding efficient analytic methods for computing, e.g., CVA, VaR and model risk. He is currently working in Global Model Validation and Governance at Deutsche Bank. He also taught evening courses on C++ and Financial Engineering at City University for seven years. Prior to that Colin worked as a developer consultant in the mobile phone industry after an extended period in academia, teaching applied maths and researching in fluid dynamics and turbulent dispersion.
Professor of Computer Science, RWTH Aachen University
Uwe Naumann: Professor of Computer Science, RWTH Aachen University
Uwe Naumann is the author of the popular text book on (Adjoint) Algorithmic Differentiation (AAD) titled “The Art of Differentiating Computer Programs” and published by SIAM in 2012. He holds a Ph.D. in Applied Mathematics / Scientific Computing from the Technical University Dresden, Germany.
Following post-doctoral appointments in France, the UK and the US, he has been a professor for Computer Science at RWTH Aachen University, Germany, since 2004. As a Technical Consultant for the Numerical Algorithms Group (NAG) Ltd. Uwe has been playing a leading role in the delivery of AAD software and services to a growing number of tier-1 investment banks since 2008.
Quantitative Analyst, Deutsche Bank
Aurelio Romero-Bermudez: Quantitative Analyst, Deutsche Bank
Analyst in the IMM group with research interests spanning from perturbative and semi-analytic approaches to risk analysis to the application of Deep Learning and adjoint differentiation in risk management. Over 8 years research experience in Theory of Condensed Matter and Quantum Gravity with a PhD in Theoretical Physics from the University of Cambridge.
Postdoctoral Research Assistant: Queen Mary University of London
Linus Wunderlich: Postdoctoral Research Assistant: Queen Mary University of London
Director of Research and Development, Riskfuel
Maxime Bergeron: Director of Research and Development, Riskfuel
Maxime Bergeron is the Director of Research and Development at Riskfuel, a capital markets focused startup that is developing ultra-fast AI-based valuation technologies. There, his work is focused on applied machine learning and the topology of high dimensional data. Prior to joining Riskfuel, he was a faculty member at the University of Chicago. He holds a PhD in Mathematics from the University of British Columbia.
Quantitative Research Solutions, Bloomberg, LP
Arun Verma: 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.
Philippe G. LeFloch:
Sorbonne University and CNRS
Philippe G. LeFloch: Research Professor, Sorbonne University
Philippe G. LeFloch holds a permanent position at Sorbonne University, as a Research Professor of the Centre National de la Recherche Scientifique. He graduated from the École Normale Supérieure (Saint-Cloud, France) and obtained a Ph.D. in Mathematics in 1988 from the Ecole Polytechnique (Palaiseau, France). In 1995, he received a Faculty Early Career Development award from the National Science Foundation. He worked at the Courant Institute of Mathematical Sciences (New York) and the University of Southern California (Los Angeles). He has published more than 200 research papers with about 100 different co-authors, and has written several textbooks, including the graduate course “Hyperbolic Systems of Conservation Laws”, Birkhäuser (2002) and a monograph establishing the “Global nonlinear stability of Minkowski space for self-gravitating massive fields’’.
Head of Quantitative Research, RavenPack
Marko Kangrga: Head of Quantitative Research, RavenPack
Marko is the Head of Quantitative Research for the Americas at RavenPack with over 10 years of experience in the finance industry. He focuses on exploring novel approaches and techniques for combining fundamental drivers with big data quantitative frameworks to identify alpha opportunities from a wide universe of securities across multiple asset classes. Previously, as the head trader/investment analyst at an event-driven hedge fund in New York, he was responsible for macro research, idea generation and risk management. Marko has experience in utilizing quantitative methods in portfolio construction, developing hedging strategies and trading structured derivative instruments.
Jean-Marc Mercier: Senior researcher, MPG-Partner, Paris
Jean-Marc Mercier is the head of the research and development team at MPG-Partners, a consulting firm for the financial services industry. He graduated from the University of Bordeaux (France) with a Ph.D. in applied mathematics obtained in 1996. He started his career as an Academic researcher, then moved to engineering in the finance industry. He is now sharing his time between various challenging industrial problems which he tackles with fundamental research tools.
PhD Student, Imperial College London
Federico Graceffa: PhD Student, Imperial College London
I am a Phd student in Applied and Financial Mathematics at Imperial College London (UK), working mainly on random dynamical systems, nonlinear valuation of financial derivatives, price impact and interest rates theory.