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“Digital Assets are fascinating to me because they are the manifestation of Satoshi’s ingenious solution to a problem in computer science that many believed was impossible to solve—the double-spending problem.”

Martin is a gifted and passionate mathematician and analyst, beginning his trajectory into the world of finance with a BSc (Hons) in Mathematics from the University of Bristol. Being aware of the advances in computing, he later enhanced this skillset by delving into Computing Science with an MSc from Birkbeck College, University of London. Later, Martin achieved a PhD on the application of machine learning to financial time series analysis from the Department of Computer Science at UCL (University College London). He has remained familiar with AI progress ever since.

Martin is a dangerously critical thinker and has always had a sharp curiosity for physics and the hard sciences. His white papers have pioneered crucial knowledge in globally sensitive topics and his laboratory experiments have helped engineer everyday consumer goods such as the tetrahedral teabag. He has worked for the Department of Land Economy, the Faculty of Economics, Judge Business School and the Computer Laboratory at the University of Cambridge. Martin has published and presented in the areas of statistics, computer science, environmental modelling, climate change, economics, finance, behavioural finance, entrepreneurial finance, trading, risk, decision making, politics and gender.

One of Martin’s ultimate aims was to apply his dexterity to financial markets, particularly as it gave him a rare opportunity to overindulge in pure mathematics outside of academia. He has worked for numerous hedge funds, including Quant Capital, where he led the research office in Cambridge, and eStats Capital.  More recently he worked as a Research and Development Specialist at nChain, conducting research in cryptography, blockchain technologies, cryptocurrencies (primarily Bitcoin) and writing white papers detailing innovations suitable as source documents for patent preparation.  

At Crypton, Martin is the Lead Quantitative Analyst. As such, he is combining his expert knowledge and research with powerful Machine Learning techniques in order to validate models, and create new high-return, non-correlated alpha strategies.


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