Helping the NHS cut waiting lists
With East Suffolk and North Essex NHS Foundation Trust, we used machine learning to predict how long operations will take, so theatre lists can be planned more accurately.
Welcome to my website
I am a Reader in the School of Computer Science and Electronic Engineering at the University of Essex, and Faculty Dean (Postgraduate) for the Faculty of Science and Health. I develop machine learning and evolutionary computation methods for finance, business and the public sector, and I have led around 30 funded projects with industry and public bodies since 2020.
I lead a research group of ten PhD students, with eight completions at Essex and Kent. I was nominated for PhD Supervisor of the Year at Essex from 2022 to 2024.
News
- Oct 2026New paper in Applied Soft Computing: Strongly-typed genetic programming with an active trading operator for algorithmic trading using fundamental, sentiment, and technical analysis.
- Sep 2026Sirui Zhu joined the research group as a PhD student.
- Sep 2026New paper in SPEAKABLE workshop at LREC 2026: On the Role of Encoder Depth: Pruning Whisper and LoRA Fine-Tuning in SLAM-ASR.
- Sep 2026New paper in IEEE Spoken Language Technology SLT 2026: CARD: Cross-component Audio Representation Distillation for Encoder-Free Audio Captioning.
- 2026New paper in Artificial Intelligence Review on machine learning for temperature forecasting in weather derivatives.
Featured work
Detecting lighthouse faults with machine learning
Since 2021 we have worked with the General Lighthouse Authorities on machine learning and machine vision that help spot faults in lighthouses and other aids to navigation early.
How can I help?
Prospective PhD students
Trading, deep RL, LLMs for finance and forecasting. See what to send me.
Industry and public sector
Knowledge Transfer Partnerships, consultancy projects and sponsored PhDs with companies and public bodies.
Media and events
Comment and talks on AI in finance, algorithmic trading and applied AI in industry.
What I work on
All researchGenetic programming for trading
Directional changes, multi-objective strategies and sentiment signals.
Deep RL for high-frequency trading
Trading agents that account for their own position and for costs.
LLMs, NLP and speech
Financial filings, sentiment signals and efficient speech models.
Forecasting
Weather derivatives, real estate and company fundamentals.
AI for the public sector
Maritime safety and NHS theatre scheduling.
Latest publications
All publicationsChristodoulaki, E., Kampouridis, M. (2026). Strongly-typed genetic programming with an active trading operator for algorithmic trading using fundamental, sentiment, and technical analysis. Applied Soft Computing, Elsevier.
Barnor, N., Kampouridis, M., Kanellopoulos, P. (2026). Machine Learning for Temperature Forecasting in Weather Derivatives. Artificial Intelligence Review, Springer.
Salman, O., Melissourgos, T., Kampouridis, M. (2026). A genetic algorithm for the optimization of multi-threshold trading strategies in the directional changes paradigm. Artificial Intelligence Review, Vol 59:2, Springer.
Organisations I have worked with
General Lighthouse Authorities, Trinity House, ESNEFT, Cooper Parry, Centerprise, Horus Security, IFE Global Logistics, Hivedome, Betascore, DOCME Technologies (now 360Me), Fingerpost Consulting, Footprint Digital, Iceni Projects, Otters.ai, PFE Express, R&B Switchgear Services, Shepherd Compello, Voono.