Emeritus Professor · Chartered Engineer · Founder

David Elliman

Combining decades of academic research in AI and pattern recognition with a passion for building software that actually works in the real world.

ORCID Publications

Background

I've worked in advanced technology industries or as an academic all my working life, often both at the same time. I started at Rolls-Royce, moved to Quest Automation, and then became an academic at the University of Nottingham, progressing to full professor and leading a successful research group in pattern recognition, neural networks, and intelligent systems.

After a stint on an interesting government AI project, I joined Monvieux Ltd — which had expanded to 100 staff by the time I left to follow a new initiative with Neuro-Symbolic Ltd. I was excited by the prospect of combining the safety of symbolic logic with the extraordinary, but unreliable, talent of large language models.

I'm interested in the development of machine learning and AI, in software engineering, and I have a side interest in quantum physics and electronics.

Career Path

  1. Now

    Founder, Neuro-Symbolic Ltd

    Building neurosymbolic AI that pairs LLM reasoning with formal verification for regulated industries.

  2. Monvieux Ltd

    Joined a newly-formed company that grew to 100 staff, working on advanced technology solutions.

  3. Government AI Project

    Two years working on an applied AI initiative for the UK government.

  4. 1988 – present

    University of Nottingham

    Progressed to Professor of Applied Computer Science, leading research in pattern recognition, neural networks, document analysis, and intelligent agents. Now Emeritus Professor.

  5. Quest Automation & Rolls-Royce

    Early career in advanced engineering and industrial automation.

Research Interests

39 publications spanning pattern recognition, neural networks, handwriting and document analysis, ontologies, constraint-based scheduling, and AI agents. A few highlights:

Pattern Recognition & Neural Networks

Shift-invariant neural nets, CMAC classifiers, hand-printed character recognition, and position-independent pattern matching.

Document Analysis & Handwriting

Arabic handwriting recognition with HMMs, graphics recognition, vectorization algorithms, and arrowhead detection.

Knowledge Representation

Ontology languages for the semantic web, XML data models, and automated document ontology derivation.

Constraint Solving & Optimisation

Genetic algorithm timetabling, graph colouring, reactive ant colony optimisation, and intelligent agent routing.

More recently: the Holographic Circlette Theory — a computational framework proposing that particle physics emerges from an 8-bit error-correcting code on a geometric lattice.

Away from the Screen

Settled in NW Gloucestershire with Jane, Rufus the Cockerpoo and Grace the British Shorthair. When I'm not wrangling language models or lattice simulations, there's always plenty to do in the garden and garage.

Interested in Neuro-Symbolic AI?

Whether you're exploring automation for regulated workflows or curious about the Circlette Theory, I'd love to hear from you.

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