Stylianos Kampakis

Stylianos Kampakis

About

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10+ years in AI & Data Science | Web3.0 tokenomics design, auditing & simulations
Barnet, United Kingdom
Contact Stylianos regarding: 
Flexible work
Starting at USD50/hour

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Résumé

Jobs verified_user 0% verified
  • Xenet AI
    Partner & CAIO
    Xenet AI
    Jan 2025 - Current (1 year 8 months)
  • Gainr
    Data Scientist and Tokenomics
    Gainr
    Feb 2024 - Current (2 years 7 months)
  • JVR Consultancy Ltd
    AI Consultant
    JVR Consultancy Ltd
    Sep 2023 - Current (3 years)
  • Janus Protocol
    Founder
    Janus Protocol
    Jul 2023 - Current (3 years 2 months)
  • Hacken Blockchain Security Auditor
    Tokenomics Audit Lead
    Hacken Blockchain Security Auditor
    Feb 2023 - Current (3 years 7 months)
  • Pollen DAO
    Algorithmic and tokenomics advisor
    Pollen DAO
    Jun 2021 - Current (5 years 3 months)
    ◘ Algorithmic design ◘ Reputation, rewards design
  • Electi Consulting
    Scientific Advisor and Head of Tokenomics
    Electi Consulting
    Apr 2018 - Current (8 years 5 months)
    Responsible for all data science aspects of Electi Consulting (https://electiconsulting.com/).
  • The Tesseract Academy
    CEO
    The Tesseract Academy
    Jan 2018 - Current (8 years 8 months)
    ◘ We help decision makers implement and understand technology, faster, easier and better. ◘ We transform organisations through AI, data science, and blockchain: from data strategy, to AI ethics, to research and development, http://tesseract.academy We provide consulting/advisory and implementation services for emerging and advanced technologies: • Data strategy • AI and data science • Blockchain and tokenomics • Project management for AI • Data-driven product development • Entrepreneurship and fundraising
  • UCL Centre for Blockchain Technologies CBT
    Research Associate
    UCL Centre for Blockchain Technologies CBT
    Nov 2017 - Current (8 years 10 months)
    Research Fellow at the UCL Centre for Blockchain Technologies. Conducting academic research on blockchain applications, as well as working on industrial initiatives.
  • US Department of State
    2017 YTILI Fellow
    US Department of State
    May 2017 - Current (9 years 4 months)
    Launched in 2016, the Young Transatlantic Innovation Leaders Initiative Fellowship is the flagship program of the Young Transatlantic Innovation Leaders Initiative (YTILI) of the U.S. Department of State, and is supported in its implementation by the German Marshall Fund of the United States (GMF). YTILI empowers young European entrepreneurs and innovators with the tools, networks, and resources they need to grow their enterprises and contribute more fully to economic development and job creation, security, and good governance in the region. YTILI is also a vehicle for building a transatlantic network of innovators that can contribute to an ongoing policy dialogue that strengthens the transatlantic relationship.
Education verified_user 0% verified
  • University of Cambridge
    Postgraduate Diploma in Entrepreneurship
    University of Cambridge
    Jan 2019 - Dec 2020 (2 years)
  • UCL
    PhD, Computer Science
    UCL
    Jan 2012 - Dec 2016 (5 years)
    Title of thesis: Predictive modelling of football injuries (thesis availalable on arXiv at https://arxiv.org/abs/1609.07480) My PhD was one of the few research projects in the world dealing with the topic of injury prediction in sports and the first one to concern football in the UK. The project was done in collaboration with Tottenham Hotspur FC. I was also one of the few students in the program to get their PhD with no corrections.
  • The University of Edinburgh
    MSc, Informatics
    The University of Edinburgh
    Jan 2011 - Dec 2012 (2 years)
    Class prize for best dissertation
  • The Open University
    Bachelor’s Degree, Mathematics and Statistics
    The Open University
    Jan 2011 - Dec 2016 (6 years)
  • The Open University
    Diploma, Economics
    The Open University
    Jan 2009 - Dec 2011 (3 years)
    Specialized in econometric analysis as part of my dissertation.
  • Aristotle University of Thessaloniki AUTH
    MSc, Advanced Computational and Telecommunication Systems (specialization: Intelligent Systems)
    Aristotle University of Thessaloniki AUTH
    Jan 2009 - Dec 2011 (3 years)
  • Aristotle University of Thessaloniki AUTH
    Bachelor of Science (BSc, Psychology
    Aristotle University of Thessaloniki AUTH
    Jan 2004 - Dec 2009 (6 years)
    Specialized in cognitive psychology and neuropsychology.
Projects (professional or personal) verified_user 0% verified
  • T
    Tottenham Hotspur Sports Analytics Platform
    May 2013 - Current (13 years 4 months)
  • S
    SHARP (Sports and Health Analytics Research Partnership) at University College London
    Dec 2012 - Current (13 years 9 months)
    The mission of SHARP is to develop innovative approaches to data collection, data storage, data analysis and visualisation to facilitate evidence-based approaches to improve human performance and health, as well as to facilitate the use of persuasive technologies to improve the quality of life in the general population.
  • T
    Tokenomics Auditing Framework
    Tokenomics Auditing Framework For Blockchain-based Start-Ups
  • A
    Algem Tokenomics Audit
    The goal of this project was to provide a tokenomics audit for Algem. Algem is a liquid staking DeFi app for Astar Network and Polkadot. The goals of this audit were: 1) Test whether Algem’s economy is robust and sustainable. 2) Understand whether Algem can experience price appreciation. 3) Investigate whether Algem is exposed to overleveraged positions which could break the overall system.
  • A
    Angelo Audit
    The purpose of this document is to provide an audit of Angelo’s token economy. The document describes any adjustments that have been made in order to improve the token economy, as well as simulations that were performed in order to test Angelo’s assumptions.
Awards verified_user 0% verified
  • A
    Awarded the PhD with no corrections
    Jun 2016
  • The University of Edinburgh
    Prize for the best dissertation
    The University of Edinburgh
    Sep 2012
    I was awarded the prize for the best dissertation in the department for the MSc in Informatics.
Publications verified_user 0% verified
  • A
    AstroFinance white paper
    May 2025
  • The British Blockchain Association
    The Tokenomics Audit Checklist
    The British Blockchain Association
    May 2023
    The Tokenomics Audit Checklist: Presentation and Examples from the Audit of a DeFi project, Terra/Luna, and Ethereum 2.0
  • S
    Prediction of Injuries in Professional Football Using Gaussian Processes with Dynamic Time Warping Kernel
    Statistical Analysis and Data Mining Sports Analytics Special Issue
    Jan 2015
  • M
    A supervised PCA logistic regression model for predicting fatigue-related injuries using training GPS data
    Mathsports International
    Jan 2015
    The goal of this thesis is to investigate the potential of predictive modelling for football injuries. This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation of Wolverhampton Wanderers (WW). Three investigations were conducted: 1. Predicting the recovery time of football injuries using the UEFA injury recordings: The UEFA recordings is a common standard for recording injuries in professional football. For this investigation, three datasets of UEFA injury recordings were available. Different machine learning algorithms were used in order to build a predictive model. The performance of the machine learning models is then improved by using feature selection conducted throug
  • U
    Using Twitter to predict football outcomes
    Jan 2014
    Twitter has been proven to be a notable source for predictive modelling on various domains such as the stock market, the dissemination of diseases or sports outcomes. However, such a study has not been conducted in football (soccer) so far. The purpose of this research was to study whether data mined from Twitter can be used for this purpose. We built a set of predictive models for the outcome of football games of the English Premier League for a 3 month period based on tweets and we studied whether these models can overcome predictive models which use only historical data and simple football statistics. Moreover, combined models are constructed using both Twitter and historical data. The final results indicate that data mined from Twitter
  • E
    Comparison of Machine Learning Methods for Predicting the Recovery Time of Professional Football Players After an Undiag
    ECML PKDD European Conference on Machine Learning and Principles and Practice of Data Mining
    Sep 2013
  • Elsevier
    Investigating the computational power of spiking neurons with non-standard behaviors
    Elsevier
    Jan 2013
    Spiking neural networks have been called the third generation of neural networks. Their main difference with respect to the previous two generations is the use of realistic neuron models. Their computational power has been well studied with respect to threshold gates and sigmoidal neurons. However, biologically realistic models of spiking neurons can produce behaviors that can be computationally relevant, but their power has not been assessed in the same way. This paper studies the computational power of neurons with different behaviors based on the previous analyses conducted by Maass and Schmitt. The studied behaviors are rebound spiking, resonance and bursting. The results of the analysis are presented. A theoretical motivation for this
  • r
    (translated from Greek) Using temporal automata for artificial grammar learning
    rd Panhellenic Conference of Cognitive Science
    Jan 2011
  • J
    Improved Izhikevich neurons for spiking neural networks
    Journal of Soft Computing SpringerVerlag
    Jan 2011
    Spiking neural networks constitute a modern neural network paradigm that overlaps machine learning and computational neurosciences. Spiking neural networks use neuron models that possess a great degree of biological realism. The most realistic model of the neuron is the one created by Alan Lloyd Hodgkin and Andrew Huxley. However, the Hodgkin–Huxley model, while accurate, is computationally very inefficient. Eugene Izhikevich created a simplified neuron model based on the Hodgkin–Huxley equations. This model has better computational efficiency than the original proposed by Hodgkin and Huxley, and yet it can successfully reproduce all known firing patterns. However, there are not many articles dealing with implementations of this model for a fun
  • t
    (translated from Greek) Using decision trees for the differential diagnosis of Alzheimer and vascular dementia
    th Panhellenic conference of Greek Neurologists
    Jan 2011
  • n
    Validity and reliability of the electronic versions of the CES-D questionnaire and the Theory of mind - Picture stories
    nd International Congress on Neurobiology Psychopharmacology Treatment Guidance
    Jan 2011