Research Publications
Research That.Reaches Production.
Our research is carried out inside two specialized labs and applied through the work we deliver. This page collects what we study, what we have already worked on, what we are working on now, and how to collaborate with our researchers.
Our Approach
Research With Somewhere to Land
Research at CodePlus Global is not separate from engineering.
Questions come out of real problems- a diagnosis that takes too long, a threat pattern nobody has characterised, a decision that cannot yet be made from the data an organisation already holds- and the answers are built to be used.
That shapes what we publish and when. Work is shared once it is grounded enough to be relied on, and in a form the people who need it can actually apply.
Areas of Research Excellence
10 Disciplines
What We Study
The disciplines our research draws on, across both labs.
- Artificial Intelligence
- Machine Learning
- Biology
- Biomedical Engineering
- Neuroscience
- Data Science
- Explainable AI
- Sociology
- Innovation
- Emerging Technologies
Where the Work Happens
Two Labs and an Academy
Research sits with the specialized companies built for it, each with its own focus and its own collaborators.
Research & Development
01
Biointelligence Labs
Advancing the future of intelligence by combining Artificial Intelligence with neuroscience, biomedical engineering, cognitive science, and next-generation computational research.
Visit Biointelligence LabsCybersecurity Division
02
AegisCyber Labs
Protecting organizations through advanced cybersecurity solutions, security research, digital defense, threat intelligence, and enterprise security consulting.
Visit AegisCyber LabsTechnology Education
03
CodePlus Academy
Developing the next generation of engineers through industry-focused education, hands-on learning, and emerging technology programs.
Visit CodePlus Academy
Previous Research
Artificial Intelligence, Machine Learning, Deep Learning
Research our labs have already carried out. Each one started as a question somebody needed answered in practice, and each was taken through the full method- literature, problem definition, data, experiment, validation.
01
Explainable Deep Learning for Clinical Decision Support
Diagnostic models built so a clinician can see which signals drove a prediction- and disagree with it. The question was whether interpretability has to be paid for in accuracy.
- Deep Learning
- Explainable AI
- Biomedical Engineering
02
Learning From Small and Imperfect Medical Datasets
Most hospitals do not hold millions of cleanly labelled records. This work examined which transfer learning and augmentation strategies still hold up on the data a regional clinical team actually has.
- Machine Learning
- Biomedical Engineering
- Data Science
03
Neural Representations of Attention and Memory
A cross-reading of neuroscience findings against the internals of deep networks- what the two fields each mean when they say a system is attending to something, and where the analogy stops being useful.
- Neuroscience
- Deep Learning
- Artificial Intelligence
04
Sequence Models for Biological Signal Data
Time-series architectures applied to physiological recordings, and the conditions under which they fail: sensor drift, missing channels, and the noise real recording environments introduce.
- Deep Learning
- Biology
- Data Science
05
Machine Learning for Threat Pattern Detection
Anomaly detection across network and endpoint telemetry, designed around the constraint that decides whether security tooling is usable at all- a team can only investigate so many alerts in a day.
- Machine Learning
- Artificial Intelligence
- Data Science
06
Evaluating Model Reliability Beyond Accuracy
Calibration, failure modes and confidence under distribution shift- the properties that decide whether a model is safe to put in front of a decision that matters, none of which a single accuracy figure reports.
- Machine Learning
- Explainable AI
- Data Science
Research In Progress
What We Are Working On Now
Sociology and Artificial Intelligence
Our current research moves the question outward. Not what a model can do, but what changes in a group of people once one is placed among them- in a classroom, a clinic, a workplace, a household.
01
We are trying to understand adoption as a social process rather than an install count- who in a group tries a system first, who becomes trusted to read what it says, and what happens to that standing the first time it is wrong.
02
We are trying to follow bias past the training set, into how a skewed decision is actually received: contested, absorbed, or quietly worked around by the people it lands on.
03
We are trying to see what automation does to the way people describe their own work, in the long period before displacement shows up in any employment figure.
04
We are trying to test explanation as a social property rather than a technical one- which kinds genuinely restore a person's willingness to overrule a machine, and which only make deferring to it easier.
This research is running now, and it has not produced findings we are ready to stand behind. When it does, they will be released through the catalogue below. We do not publish a conclusion before it is grounded enough to be relied on.
Method
From Open Question to Deployable Knowledge
Every piece of research follows the same disciplined path- discovery, literature review, a precise problem definition, data, experimental design, development, validation, and knowledge transfer. It is the method our Research & Strategy practice runs for clients, and the one our own labs work to.
See the full methodologyAvailable Now
How Our Research Reaches You Today
The formal publication catalogue is in preparation. In the meantime, our research already reaches people through three routes- all of them live.
01
Research & Strategy Engagements
Applied research delivered directly to an organisation- AI readiness assessments, market and user research, competitor analysis and technology roadmaps.
Explore the practice02
Case Studies
Detailed write-ups of real projects: the problem, the approach taken, the technologies involved and what changed as a result.
Read the case studies03
Insights & Articles
Shorter written work from our engineers and researchers- the arguments, methods and trade-offs behind how we build.
Read the articles
In Preparation
The Publication Catalogue
We are preparing a public catalogue of technical reports and research papers from both labs. It is not published yet.
If you are a researcher, a university, a hospital or a technology partner who would like to be told when it opens- or who needs to discuss specific work before then- our research team will hear from you directly.
Email the research teamCollaboration
Who We Work With
We welcome collaborations with universities, hospitals, researchers, and technology organizations.
- Universities
- Hospitals & Clinical Teams
- Independent Researchers
- Technology Organizations
- Government & Public Bodies
- Industry Partners
Let's Build
Have a Research Question?
Whether you're exploring a research partnership, assessing your organisation's readiness for AI, or looking for a team that can take a question from hypothesis to production, CodePlus Global is ready to help.
