Archie Norman
CEO at Mercury Labs AI
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolArchie Norman is a data-driven CEO and founder with a decade of experience building AI and analytics-led products, currently leading Mercury Labs AI and co-founding Augmentive. Trained in computer science and big data (MSc, UCL) with fintech blockchain study at Oxford, he combines academic rigor—his distinction thesis applied supervised ML to deanonymise Bitcoin transactions—with hands-on engineering as a former lead data scientist and Python engineer. He has repeatedly moved from research to product across startups and scale-ups, shaping data platforms and ML pipelines in roles from Playbrush to FRST. Based in London, he blends entrepreneurial leadership with deep technical chops in complex networks, distributed systems and cloud architectures, and brings a privacy-conscious perspective shaped by real-world blockchain analysis.
10 years of coding experience
4 years of employment as a software developer
Fintech Programme Blockchain, Fintech Programme Blockchain at Saïd Business School, University of OxfordBachelor of Science (BSc) Computer Science, Bachelor of Science (BSc) Computer Science at Newcastle UniversityUppingham SchoolUniversity College London
Stackoverflow
1,444reputation
151kreached
3answers
41questions
amazon-web-services
top-5%
flask
top-5%
mod-wsgi
top-5%
Github Skills (199)
spider10
crawler10
popularity10
scraper10
svg-graph9
golang9
connected-components9
forex9
financial-markets9
react-redux9
redux-thunk9
network-protocol9
flask-restful9
flask9
redux-router9
Programming languages (9)
Github contributions (5)
archienorman11/machine-learning-models
Jun 2016 - Mar 2023
Linear and Logistic Regression Python Implementation
Contributions:1 PR, 3 pushes, 1 branch in 6 years 10 months
archienorman11/thesis-bitcoin-clustering
Sep 2016 - Jul 2017
The Bitcoin currency is a publicly available, transparent, large scale network in which every single transaction can be analysed. Multiple tools are used to extract binary information, pre-process data and train machine learning models from the decentralised blockchain. As Bitcoin popularity increases both with consumers and businesses alike, this paper looks at the threat to privacy faced by users through commercial adoption by deriving user attributes, transaction properties and inherent idioms of the network. We define the Bitcoin network protocol, describe heuristics for clustering, mine the web for publicly available user information and finally train supervised learning models. We show that two machine learning algorithms perform successfully in clustering the Bitcoin transactions based on only graphical metrics measured from the transaction network. The Logistic Regression algorithm achieves an F1 score of 0.731 and the Support Vector Machines achieves an F1 score of 0.727. This work demonstrates the value of machine learning and network analysis for business intelligence; on the other hand it also reveals the potential threats to user privacy.
Contributions:10 commits, 8 pushes, 1 branch in 9 months
heuristicsbusiness-intelligencevectorfinancethreats
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