Prof Uwe Aickelin
Head, School of Computing and Information Systems
School of Computing and Information Systems
362 Scholarly works
11 Projects
HIGHLIGHTS
2026
Journal article
Addressing Small Data Challenges in Biopharmaceutical Development and Manufacturing: A Mini Review of Multi-Fidelity Techniques
DOI: 10.1002/bit.702132026
Conference Proceedings
Improving Emergency Department Decision-Making Through High-Cardinality Feature Integration
DOI: 10.1007/978-3-032-10685-8_92024
Conference Proceedings
ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification
DOI: 10.1145/3637528.36718622022
Research grants (ARC, NHMRC, MRFF)
ARC Research Hub for Digital Bioprocess Development
2021
Research contracts (non-grants)
Faster, Smarter Pharma and Food Manufacturing
2021
Research Contracts
Faster, Smarter Pharma and Food Manufacturing
2021
Journal article
A review on human–ai interaction in machine learning and insights for medical applications
DOI: 10.3390/ijerph18042121
RECENT SCHOLARLY WORKS
2026
Book Chapter
Efficient Selection of Low-Fidelity Data for Multi-fidelity Surrogate Models
DOI: 10.1007/978-981-95-7072-0_322026
Conference Proceedings
Order-Aware Metric Learning for Electronic Health Records Applications
DOI: 10.5220/00143160000040702026
Conference Proceedings
Using Machine Learning to Identify Patient-Ventilator Asynchrony during Non-Invasive Ventilation in Patients with Motor Neurone Disease
DOI: 10.1016/j.sleep.2025.1086372026
Conference Proceedings
SegPPMTS: Unsupervised Segmentation for Pseudo-periodic Medical Time Series
DOI: 10.1007/978-981-92-1300-9_462025
Journal article
Breathing Cycle-Aware Segmentation for Patient-Ventilator Asynchrony Detection
DOI: 10.1109/JBHI.2025.36192692025
Journal article
Development of a Machine Learning Model for Predicting Treatment-Related Amenorrhea in Young Women with Breast Cancer
DOI: 10.3390/bioengineering121111712025
Journal article
Uncertainty-aware non-invasive patient–ventilator asynchrony detection using latent Gaussian mixture generative classifier with noisy label correction
DOI: 10.1007/s41060-024-00556-32025
Dataset
400,000 in-silico CHO-K1 fed-batch runs with different media concentrations, feeding strategies, and initial seeding densities
DOI: 10.26188/28943096