I build machine learning models that keep working when data is scarce, noisy or shifted.
Much of my work brings topology into deep learning: persistent homology, cell and simplicial complexes and hypergraphs give a network structure it can rely on when pixel-level cues fail. I pair this with hyperspectral and multimodal sensing, where I started, and apply it to crops and food, medical imaging, industrial inspection and radar.
I am a Research Fellow at La Trobe University with adjunct appointments at CSIRO and the Australian National University. I co-lead the CSIRO ML Workshop Series, supervise PhD and honours students, and received my PhD from Griffith University for work on 3D computer vision from hyperspectral images.
TopoOT chains optimal transport across persistence diagrams from different thresholds and filtrations, then trains a light head online on the features that stay stable. Up to +24.1% mean F1 over the next best method on 2D benchmarks.
TopoTTA replaces threshold heuristics with persistent homology on anomaly maps, producing pseudo-labels that keep defects connected. About 15% higher F1 on average across five 2D and 3D benchmarks.
MM-HCAN builds hypergraphs within and across sensor modalities and trains them contrastively, diagnosing bearing, stator and rotor faults together. Up to 99.82% accuracy with strong cross-domain transfer.
Learnable hypergraphs and cross-view optimal transport for multi-view radar semantic segmentation.
Persistent-homology pseudo-labels for test-time adaptation in anomaly segmentation.
Hypergraph contrastive fusion of vibration signals and spectrograms for motor fault diagnosis.
RGB and point cloud fusion with attention-guided restoration for industrial anomaly detection.
Plus 7 hyperspectral computer vision repositories in MATLAB from my PhD and related projects.
All code github.com/alizia369