Peer-reviewed foundations

Research & Publications

Published research in machine learning, statistics and token economics by Dr Stelios Kampakis, CStat (Chartered Statistician, PhD in Computer Science, UCL) — and what each piece of it means for the organisations we advise. We do not deploy generic AI: our frameworks come from work that survived peer review.

Books

The Decision Maker's Handbook to Data Science

Apress / Springer Nature · editions 2020 & 2024 · Dr Stelios Kampakis
What this means for you: The playbook we use with executive clients — how non-technical leaders evaluate vendors, hire the right people, and build a data & AI strategy that actually gets adopted. The 2024 edition adds AI ethics: bias, fairness, transparency and accountability.
Peer-reviewed research

The Tokenomics Audit Checklist

The Journal of The British Blockchain Association · 2023 · with Linas Stankevičius
What this means for you: A published, repeatable framework for auditing a token economy — the same method we apply to client projects, stress-tested in the paper on a DeFi project, Terra/Luna and Ethereum 2.0.

Auditing Tokenomics: Lessons from Auditing a Stablecoin Project

The Journal of The British Blockchain Association · 2022 · Dr Stelios Kampakis
What this means for you: Proof the audit method works on live systems — a real stablecoin engagement, turned into peer-reviewed guidance on where token economies fail and how to check them.

Why Do We Need Tokenomics? & Three Case Studies in Tokenomics

The Journal of The British Blockchain Association · 2018 · Dr Stelios Kampakis
What this means for you: Two of the field's early formal arguments that token economies need real economic design — including three actual token-design engagements and how each was solved.
Doctoral research & selected preprints

Predictive Modelling of Football Injuries

PhD thesis, Dept. of Computer Science, UCL · 2016 (defended)
What this means for you: Machine learning on messy, real-world operational data — training loads, GPS traces, medical records — built in collaboration with elite football clubs including Tottenham Hotspur. The origin of our "production ML on imperfect data" practice.