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.
Systematic Trading, JPMorgan Chase & Co
Ivan Zhdankin: Systematic Trading, JPMorgan Chase & Co
Ivan Zhdankin is a quantitative researcher with experience in diverse areas of quantitative finance, including risk modelling, XVA, and electronic trading across asset classes, including commodity futures and G10 and emerging market currencies. Ivan was consulting various banks in quantitative modeling and has recently joined JP Morgan as a quantitative analyst. He has become one of the first researchers to generate convincing results in electronic alpha with neural nets. He has a solid mathematical background from New Economic School and Moscow State University, where he studied under the celebrated Albert Shiryaev, one of the developers of modern probability theory.
Founder and Principal, Benzschawel Scientific, LLC
Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC
Terry Benzschawel is the Founder and Principal of Benzschawel Scientific, LLC. The former Managing Director in Citigroup’s Institutional Clients Business. Terry headed the Credit Trading Analysis group which develops and implements quantitative tools and strategies for credit market trading and risk management, both for Citi’s clients and for in-house applications. Some sample tools include models of corporate default and recovery values, relative value of corporate bonds, loans, and credit default swaps, credit portfolio optimization, credit derivative trades, capital structure arbitrage, measuring and hedging liquidity risk, and cross-credit-sector asset allocation.
After six years of post-doctoral research in academia and industry and two years in consumer banking, Terry began his investment banking career in at Salomon Brothers in 1992. Terry built models for proprietary arbitrage trading in bonds, currencies and derivative securities in Salomon’s Fixed Income Arbitrage Group. In 1998, he moved to the Fixed Income Strategy department as a credit strategist with a focus on client-oriented solutions across all credit markets and has worked in related roles since then. Terry was promoted to Managing Director at Citi in 2008.
Terry received his Ph.D. in Experimental Psychology from Indiana University (1980) and his B.A. (with Distinction) from the University of Wisconsin (1975). Terry has done post-doctoral fellowships in Optometry at the University of California at Berkeley and in Ophthalmology at the Johns Hopkins University School of Medicine and was a visiting scientist at the IBM Thomas J. Watson Research Center prior to embarking on a career in finance. He currently serves on the steering committees of the Masters of Financial Engineering Programs at the University of California at Berkeley and the University of California at Los Angeles and Carnegie Mellon University’s Computational Finance Program.
Terry is a frequent speaker at industry conferences and events and has lectured on credit modelling at major universities. In addition, he has published over a dozen articles in refereed journals and is author of CREDIT MODELING: FACTS, THEORIES AND APPLICATIONS. In addition, Terry has been the instructor for courses in credit modelling for Incisive Media and the Centre for Finance Professionals. Finally, Terry has taught a course on credit modelling at Russia’s Sberbank in Moscow.
Principal Manager, Cloud & AI & Machine Learning, Microsoft
Francesca Lazzeri: Principal Manager, Cloud & AI & Machine Learning, Microsoft
Francesca Lazzeri is a machine learning scientist on the cloud advocacy team at Microsoft. An expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems, she has worked with these issues in a wide range of industries, including energy, oil and gas, retail, aerospace, healthcare, and professional services. Previously, she was a research fellow in business economics at Harvard Business School, where she performed statistical and econometric analysis within the Technology and Operations Management Unit and worked on multiple patent data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation. Francesca periodically teaches applied analytics and machine learning classes at universities in USA and Europe. and is a mentor for PhD and postdoc students at the Massachusetts Institute of Technology. She enjoys speaking at academic and industry conferences to share her knowledge and passion for AI, machine learning, and coding. Francesca holds a PhD in innovation management.
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.
Software Engineer, NAG
Jonathan Boyle: Software Engineer, NAG
Jonathan is a scientist and research software engineer with over 15 years’ experience of high-performance computing. He has contributed to various software projects written in C, C++, Fortran and Python. Most recently, Jonathan has been working at NAG as a software engineer on the EU funded POP project. This work offers services to improve the performance of parallel software, written in a range of languages (including Python), designed to run on HPC hardware, including the world’s largest supercomputers.
Lecturer, King’s College London and Researcher, The Alan Turing Institute
Blanka Horvath: Lecturer, King’s College London and Researcher, The Alan Turing Institute
Blanka is a Honorary Lecturer in the Department of Mathematics at Imperial College London and a Lecturer at King’s College London. Her research interests are in the area of Stochastic Analysis and Mathematical Finance.
Her interests include asymptotic and numerical methods for option pricing, smile asymptotics for local- and stochastic volatility models (the SABR model and fractional volatility models in particular), Laplace methods on Wiener space and heat kernel expansions.
Blanka completed her PhD in Financial Mathematics at ETHZürich with Josef Teichmann and Johannes Muhle-Karbe. She holds a Diploma in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong.
Adriano Soares Koshiyama:
Research Fellow in Computer Science, University College London
Adriano Soares Koshiyama: Research Fellow in Computer Science, University College London
Recently, an intern at the AI Labs in Goldman Sachs, working as a Machine Learning Strats. Nowadays he is part of the Alan Turing Institute as a Enrichment Scheme Student. Its main research topics are related to Data Science, Machine Learning, Statistical Methods, Optimization, and Finance. A PhD Candidate in Computer Science at University College London (UCL) in the topic of Financial Computing and Analytics. Adriano has a Bachelor’s Degree in Economics from UFRRJ and a Master’s in Electrical Engineering from PUC-Rio.
HPC Software Engineer, NAG
Phil Tooley: HPC Software Engineer, NAG
Phil is an HPC Software Engineer at NAG with a wide range of experience in both academic and industrial HPC projects. His interests include high performance computing, machine learning and systems performance analysis, particularly in the cloud. Day to day he works on a wide range of projects in these areas helping customers achieve maximum performance and efficiency from their HPC deployments.
Phil began his career studying computational plasma physics at the University of Strathclyde, where he developed large-scale HPC applications using MPI and OpenMP. He quickly discovered that writing great code was his passion and moved fully into the world of high performance computing. Along with traditional HPC development, Phil also has a keen interest in distributed machine learning and other GPU-accelerated workloads which come with their own unique set of performance and optimisation challenges. He is also the author of the PyPOP performance analysis tool which is used by the EU Performance, Optimisation and Productivity (POP) Center of Excellence for MPI and OpenMP performance analysis.