Paul Bilokon, PhD

CEO and Founder of Thalesians Ltd. Previously served as Director and Head of global credit and core e-trading quants at Deutsche Bank, the teams that he helped set up with Jason Batt and Martin Zinkin. Having also worked at Morgan Stanley, Lehman Brothers, and Nomura, Paul pioneered electronic trading in credit with Rob Smith and William Osborn.

Paul has graduated from Christ Church, University of Oxford, with a distinction and Best Overall Performance prize. He has also graduated twice from Imperial College London.

Paul’s lectures at Imperial College London in machine learning for MSc students in mathematics and finance and his course consistently achieve top rankings among the students.

Paul has made contributions to mathematical logic, domain theory, and stochastic filtering theory, and, with Abbas Edalat, has published a prestigious LICS paper. Paul’s books are being published by Wiley and Springer.

Dr. Bilokon is a Member of British Computer Society, Institution of Engineering and Technology, and European Complex Systems Society.

Paul is a frequent speaker at premier conferences such as Global Derivatives/QuantMinds, WBS QuanTech, AI, and Quantitative Finance conferences, alphascope, LICS, and Domains.

Saeed Amen

Saeed Amen is the founder of Cuemacro. Over the past fifteen years, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura. He is also the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan) and is the coauthor of The Book of Alternative Data (Wiley), due in 2020.

Through Cuemacro, he now consults and publishes research for clients in the area of systematic trading. He has developed many Python libraries including finmarketpy and tcapy for transaction cost analysis. His clients have included major quant funds and data companies such as Bloomberg. He has presented his work at many conferences and institutions which include the ECB, IMF, Bank of England, and Federal Reserve Board.

He is also a co-founder of the Thalesians.

Prof. Matthew Dixon

Assistant Professor in the Applied Math Department at the Illionois Institute of Technology. His research in computational methods for finance is funded by Intel.

Matthew began his career in structured credit trading at Lehman Brothers in London before pursuing academics and consulting for financial institutions in quantitative trading and risk modelling.

He holds a PhD in Applied Mathematics from Imperial College (2007) and has held postdoctoral and visiting professor appointments at Stanford University and UC Davis respectively.

He has published over 20 peer reviewed publications on machine learning and financial modelling, and has been cited in Bloomberg Markets and the Financial Times as an AI in fintech expert.

Together we can achieve incredible things. Like, for example, having a much more transparent financial system, the cornerstone of a healthy, functioning democracy. It’s all about sharing ideas in the spirit of innovation, the spirit of helping others.

Prof. Matthew Dixon, Deepmind and the Future of the Finance Industry, TEDx talk


Harvey J. Stein

Harvey J. Stein is Head of Quantitative Risk Analytics Group at Bloomberg, responsible for all quantitative aspects of Bloomberg’s risk analysis products. Dr Stein graduated from Worcester Polytechnic Institute in 1982 with a Bachelor’s degree in mathematics. After working at Bolt, Beranek and Newman for three years on developing and designing the precursor to the Internet, Dr Stein went to graduate school at the University of California, Berkeley, where he studied arithmetical geometry while working at Wells Fargo Investment Advisors. He received his PhD in mathematics from Berkeley in 1991.

For the last twenty-three years, Dr Stein has worked at Bloomberg LP. He built one of the top quantitative finance research and development groups in the industry. His group supplied derivative valuation methods for interest rate derivatives, mortgage backed securities, foreign exchange, credit, equities, and commodities, and built Linux clusters to supply these valuations to Bloomberg’s customers.

Dr Stein is well known in the industry, having published and lectured on mortgage backed security valuation, CVA calculations, interest rate modelling, credit exposure calculations, and other subjects. Dr Stein built Bloomberg’s business in the area of counterparty credit risk modelling and is currently focussing on regulation and risk modelling. He is also a member of the advisory board of the IAQF, an adjunct professor at Columbia University, and a board member of the Rutgers University Mathematical Finance programme and of the NYU Enterprise Learning programme.

Fazlynn Azrul

Fazlynn Azrul has graduated from Goldsmiths, University of London, with a MSc in Consumer Behaviour with a Distinction for her research paper / dissertation “CEOs Archetypes on Twitter”. The paper explores the current phenomenon of CEOs being vocal on Twitter and how their tweets reflect their personality and reputation.

In this masters programme, she studied the Psychology of Advertising and Marketing, Consumer Behaviour, Customer Experience, Marketing Strategy, Innovation Case Studies, Digital Research Methods, Marketing Analytics, Design Thinking, Digital Branding, Leadership & Talent Management, as well as Statistics.


Dorian Guzu

Dorian Guzu is pursuing his PhD in pure mathematics at Imperial College London under the supervision of Prof. Ari Laptev.

His research involves numerical methods in quantum mechanics and applied inequalities optimisation.

In 2017 he won the Faculty of Natural Sciences Prize for Excellence in the Support of Teaching and Learning and in 2019 he has been nominated for the Student Academic Choice Awards as a result of his outstanding work in the Mathematics Department.

He has worked with the Learning technology team for the Faculty of Natural Sciences, where he provided suggestions for better administration of the College learning modules.

Brian Healy, PhD

Brian has a PhD in Quantitative Finance and is an Adjunct Professor in Financial and Risk Engineering at New York University as well as being an Adjunct Professor of Mathematical Finance at Imperial College London.

He is an expert in solving complex problems using advanced mathematics and technology with a particular passion for the identification, analysis, pricing and management of risk.

He has held credit risk modelling positions for banks and asset managers, been Chief Scientific Officer for a number of enterprises with a focus on machine learning and artificial intelligence and was a mathematical modelling and exotic options trader for a number of tier 1 banks.

Blanka Horvath, PhD

Blanka is an 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 has co-authored a paper introducing one of the first applications of neural networks to mathematical finance, facilitating the calibration of rough volatility models.

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.

Aitor Muguruza Gonzalez

Aitor is a PhD student in mathematical finance at Imperial College London.

He is currently working on the QR Equities division at Natixis. His current research focuses on theoretical and numerical analysis of rough volatility models and their applications in finance.

He was awarded the 2017 Natixis Foundation for Research and Innovation prize for best Master’s thesis.

With Blanka Horvath, he has co-authored a paper introducing one of the first applications of neural networks to mathematical finance, facilitating the calibration of rough volatility models.

Jan Novotny, PhD

A front office quant in the eFX markets working on predictive analytics and alpha signals.

Jan has built up a quantitative offering at HSBC. Prior to joining HSBC, he was working in the Centre for Econometric Analysis on the high-frequency time series econometric models and was visiting lecturer at Cass Business Group, Warwick Business School and Politecnico di Milano.

He co-authored a number of papers in peer-reviewed journals in Finance and Physics, contributed to several books, and presented at numerous conferences and workshops all over the world. During his PhD studies, he co-founded Quantum Finance CZ.

Henry Sorsky

Henry is a quant for a proprietary sports trading firm having previously studied Mathematical Finance at Imperial College London.

Before his current position, Henry worked for Capstone Investment Advisors in their fixed income team and IHS Markit, where he completed his MSc thesis on factor investing in the automotive industry using machine learning.

Henry has experience programming in Python, C++ and R, along with Cuda and has a particular interest in filtering techniques for signal processing.

Ivan Zhdankin

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 modelling 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.