Instructors

Dr. Paul A. Bilokon

Dr. Paul A. Bilokon

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.

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

Dr. Nick Firoozye

Dr. Nick Firoozye

Dr. Nick Firoozye is a mathematician and statistician with over 20 years of experience in the finance industry, in both buy and sell-side firms, largely in  research.

Dr. Firoozye started his career in Lehman Brothers doing MBS/ABS modelling, heading teams in portfolio strategy and EM quant research, later taking a variety of senior roles at Goldman Sachs, and Deutsche Bank, as well as at the asset managers, Sanford Bernstein, and Citadel, in areas ranging from quantitative strategy, relative value strategy and trading, to fixed income asset allocation.

He was an MD and Head of Global Derivative Strategy, part of the Quantitative Investment Strategy Group, at Nomura from 2009-2017 and more recently Head of Quantitative Strategy at Symmetry Investments. He is currently an Honorary Senior Lecturer in Computer Science at University College London, focusing on Robust Machine Learning in finance.

Dr. Nick Firoozye co-authored a book, entitled Managing Uncertainty, Mitigating Risk, about the role of uncertainty and imprecise probability in finance, in light of the many recent financial crises, and he is writing a book on Algorithmic Trading Strategies based on his PhD and MSc courses on the same topic offered at UCL.

Dorian Guzu

Dorian Guzu

Dorian is currently studying for his PhD degree focused in Mathematics, where he manifests research interests involving Numerical Analysis, Optimisation, and Quantum Mechanics. During his studies he has demonstrated high quality tutoring service in the Mathematics department from Imperial College by winning the Faculty of Natural Sciences Prize for Excellence in the support of Teaching and Learning.

Dr. Brian Healy

Dr. Brian Healy

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.

Jacob Russell Holley

Jacob Russell Holley

Doctoral Candidate (PhD) and Roth Scholar in the Department of Applied Mathematics at Imperial College London. His research interests are in the areas of machine learning and asymptotic analysis.

Jacob combines approaches from deep learning, modern asymptotics, uncertainty quantification and Bayesian statistics. His current work includes a New Hybrid CNN-RNN Architecture for Non-Linear Multidimensional Spatio-Temporal Data.

He has a wealth of expertise in machine learning software, developing and implementing new TensorFlow and Keras classes.

Jacob also holds an MSc in Applied Mathematics with Distinction from Imperial College London.

Dr. Blanka Horvath

Dr. Blanka Horvath

Blanka is an Honorary Lecturer in the Department of Mathematics at Imperial 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.

Hemant Khatri

Hemant Khatri

Hemant is a final year PhD student in the department of mathematics at Imperial College London. His research concerns with the study of large-scale dynamics and turbulence in the atmosphere and oceans based on idealised model simulations and observational datasets. Previously, he completed his MSc in atmospheric and oceanic sciences from Indian Institute of Science, India, and bachelors in chemical engineering from BITS Pilani, India.

Alexandra Mostovoy

Alexandra Mostovoy

Alex recently graduated from the University of Edinburgh, where she studied Astrophysics. Since then, she’s worked for a Scottish tech start-up, imparted a wealth of mathematical knowledge and technique onto young minds by way of tutoring, and is currently a Data Scientist for Thalesians.

Alex speaks six languages (human) to varying degrees of fluency, and is gaining proficiency in communicating with machines in Java. She thrives on solving mathematical problems and delights in using vector calculus and matrix algebra.

Dr. Jan Novotny

Dr. Jan Novotny

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.

Paula Rowińska


Paula Rowińska

Paula Rowińska is a PhD student of the Mathematics of Planet Earth Centre for Doctoral Training. Her research interests include statistics, financial mathematics, and stochastic analysis, applied to areas such as energy finance and ecology. Currently she is studying how renewable energy sources influence electricity prices.

She also actively engages in science communication. She has published in a variety of popular science magazines and websites, as well as regularly blogs about maths and science (www.paularowinska.wordpress.com). In November 2017 she gave a TEDx talk “Let’s have a maths party!” at TEDx Wandsworth.

After her successes in science communication competitions such as FameLab Poland (national finalist) and Three Minute Thesis (first prize), she was invited to many radio stations and podcasts to talk about maths and mathematicians.

Simon Schoeller

Simon Schoeller

Simon Schoeller is a PhD student in Applied Mathematics at Imperial College London, and currently also a teaching assistant at the London School of Economics.

His research is mainly concerned with numerical methods for simulating the dynamics of elastic fibres and cells in fluids. This requires high-performance computing and analysis of moderately large datasets.

He holds degrees in physics from Imperial College London and ETH Zurich.

Henry Sorsky

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

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.