Professor
Amandeep Sidhu.
Making intelligence more human — from the architecture of biomedical knowledge to the institutions that will shape an AI-enabled future.
From data
to decisions.
Professor Sidhu’s work has long asked a practical question: how do we make information useful without losing sight of the people it affects? His research in biomedical ontologies and health data helped give complex knowledge a structure that computers can use and humans can scrutinise.
That same question now reaches into AI governance, skills and education. In a Thomson Reuters conversation with leaders from Microsoft, L’Oréal and the legal profession, he argued that decisions made by generative AI in education and healthcare must remain inspectable and overridable. Read the conversation
Three questions worth asking.
Where technology, learning and institutions meet.
Responsible AI & emerging tech
Who is accountable when technology moves faster than policy?
Advocating practical governance, data ethics and human oversight so innovation earns trust instead of assuming it.
Skills modernisation
How do we recognise what people can actually do?
Exploring clearer, more agile pathways for learning and professional growth, including competency frameworks such as SFIA.
Industry-sector collaboration
What changes when institutions build together?
Connecting academia, industry and government to make qualifications more relevant and careers more accessible.
SFIA is the Skills Framework for the Information Age. This section describes an editorial focus, not a claim of authorship or formal affiliation with SFIA.
Ideas with
roots.
Selected publications from his Google Scholar record, spanning biomedical knowledge, diagnosis and data integration. Citation rankings change; explore the live record for current figures.
View full research record- 01Biomedical knowledge
Toward a Spinal Cord Ontology
A shared language for describing the spinal cord — foundational work on making complex biological information easier to organise, compare and connect.
- 02AI in diagnosis
Machine Learning Classification Techniques for Breast Cancer Diagnosis
A comparison of machine-learning approaches to classifying breast cancer data, illustrating both the promise and the responsibility of decision support in healthcare.
- 03Bioanalytical methods
SELEX Modifications and Bioanalytical Techniques for Aptamer–Target Binding Characterization
A review of how molecular probes are selected and their binding measured — research that supports more precise approaches to biological detection.
- 04Scientific data infrastructure
Protein Ontology Development using OWL
An early contribution to making protein information machine-readable and interoperable, long before trustworthy AI became a mainstream demand.
Knowledge
in print.
Data, Semantics and Cloud Computing
Professor Sidhu is Editor-in-Chief of this Springer book series, which connects big data, semantic web technologies and cloud computing. Its books range from ontologies and data integration to privacy, security and large-scale computing — an extended conversation about how knowledge travels between systems and people.
Explore the book series at Springer“Irrespective of the sector, it [AI] is augmenting your job, your task; it is not ever replacing that.”
Professor Amandeep Sidhu · Thomson Reuters TechFuture, 2023
Make decisions inspectable. Keep people able to intervene.
In the same discussion, Sidhu emphasised human oversight in education and healthcare and warned that even anonymised health data can remain sensitive. Responsible AI, in his account, is a continuing practice of checking bias, learning from mistakes and protecting the people represented in the data.
Read the Thomson Reuters discussion
Sequencing the
past in the cloud.
iTnews, September 2011 · Reported by James Hutchinson.
Read the iTnews articleLong before cloud computing was routine in research, iTnews profiled his work at Curtin University: a six-month trial running DNA genome sequencing of extinct animal species on Microsoft Azure — data flowing in a hybrid cloud between Azure nodes in Singapore, on-campus high-performance computing clusters and a genome sequencer at Royal Perth Hospital. Analyses that had taken weeks on national compute facilities were finishing in hours, as the team worked through genomes including an extinct breed of Peruvian alpaca with no reference sequence to compare against.
“The hardest part is when you do not have a reference, you have no idea where you’re going… the closest we can come is the descendants of the species, its closest possible neighbour in the chain.”
He described the case for on-demand infrastructure plainly: “Where Azure and HPC works pretty well is the on-demand compute and storage… you get the flexibility of scaling up an Azure node pretty easily and vary the size of the compute and storage that you need.” The article also noted his dual role as systems engineer and adjunct research fellow — a deliberate bridge between researchers and IT that Curtin went on to introduce across every faculty.
“At an operational level, seconding [Sidhu] into our team provided a real researcher’s mindset to help build our capability… the link between what he does and his research peers gets a significant boost.”