PhD Candidate · Adelaide, Australia

Eagle W. H. Liang

AI research for Trust, Safety, Security & Privacy — teaching models to know what they don't know.

Australian Institute for Machine Learning (AIML) · SA Pathology
The University of Adelaide

w.liang@adelaide.edu.au Google Scholar GitHub

// News

Nov 2025
Two papers presented at CIKM 2025, Seoul — presented our work on calibration for Kolmogorov–Arnold Networks and for medical segmentation models.
Sep 2025
CIKM 2025 Travel Award — awarded by the CIKM 2025 Organizing Committee in recognition of accepted papers and contributions, supporting participation and presentation at CIKM 2025 in Seoul, Korea.
Aug 2024
FinSen dataset released — a financial news & sentiment dataset spanning 197 countries. Get it on GitHub ↗

// Publications Google Scholar ↗

2025

Calibrating on Kolmogorov–Arnold Networks Wenhao Liang, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen

CIKM 2025 · Seoul arXiv
We Care Each Pixel: Calibrating on Medical Segmentation Models through Signed Distance Wenhao Liang, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen

CIKM 2025 · Seoul arXiv
TraffiX-MoE: A Traffic-Aware Neural VRP Solver Wenhao Liang, Lin Yue, Wei Emma Zhang, Joy Rathjen, Peter O'Loughlin, Weitong Chen

ADMA 2025 · Kyoto · Industry Track

2024

Enhancing Financial Market Predictions: Causality-Driven Feature Selection Wenhao Liang, Zhengyang Li, Weitong Chen Integrates financial news and stock-market data across 197 countries with LSTM models to improve market-prediction accuracy.

ADMA 2024
Correlation Analysis of Adversarial Attack in Time Series Classification Zhengyang Li, Wenhao Liang, Chang Dong, Weitong Chen, Dong Huang Investigates how time-series classifiers process local vs. global information under adversarial attack.

ADMA 2024

// Research Scope: Model Optimization

Model Calibration

Uncertainty estimation, temperature scaling, and focal-loss extensions for imbalanced data — making confidence mean something.

Efficient Training & Inference

Quantization, pruning, knowledge distillation, neural architecture search, and memory-efficient training.

Fairness, Robustness & Privacy

Adversarial robustness, differential privacy, and federated learning for trustworthy models.

Hyperparameter Optimization

Bayesian optimization, genetic algorithms, and multi-objective search for peak performance.

Optimization Algorithms

Advanced optimizers, second-order methods, and adaptive learning rates for convergence and stability.

Meta- & Few-Shot Learning

Rapid task adaptation with minimal data via MAML and optimization-based meta-learning.

Interpretability & Explainability

Saliency maps, SHAP values, and post-hoc explanation techniques for transparent models.

Causal Reasoning

Causal inference for cause-and-effect discovery, robustness, and generalization.

Transfer Learning & Fine-Tuning

Pretraining/fine-tuning pipelines, domain adaptation, and continual learning.

Hardware-Aware Optimization

Edge computing, TinyML, and models tuned for GPUs, TPUs, and FPGAs.

Reinforcement Learning

Policy optimization and model-free vs. model-based trade-offs.

Emerging Trends

Quantum machine learning, SAM 2, and neurosymbolic AI combining neural nets with symbolic reasoning.

// Research Group — DT Lab

The Data Transpose (DT) Lab at The University of Adelaide focuses on AI, big data, and cloud-computing research aimed at transforming industries through cutting-edge technology. 📍 The University of Adelaide, North Terrace, Adelaide, SA

Lab Leader

A/Prof. Wei Emma Zhang

Dr. Wei Emma Zhang is an Associate Professor at the School of Computer Science and Information Technology and a researcher at the Australian Institute for Machine Learning (AIML), Adelaide University. She holds an ARC Early Career Industry Fellowship and is lead investigator of an ARC Discovery Project and an ARC Linkage Project. Author of 140+ publications across NLP, text mining, machine learning, and distributed systems, she is a recipient of the South Australia Young Tall Poppy Award (2024) and the Women of Colour in STEM Award (2025). She received her PhD in Computer Science from the University of Adelaide in 2017 and is an Honorary Lecturer at Macquarie University.

Lab Leader · DT-Lab Co-Director

Dr. Tony Weitong Chen

Dr. Tony Weitong Chen is a Senior Lecturer at Adelaide University, a core researcher at the Australian Institute for Machine Learning (AIML), and holder of the prestigious ARC Early Career Industry Fellowship. His research develops advanced machine-learning frameworks for medical data, multimodal information fusion, and knowledge discovery from imperfect data, with over $2.1 million in funding secured from the ARC, the Australia Economic Accelerator, and industry and government partners. He serves as Associate Editor of Neural Networks and Array, and earned his PhD from the University of Queensland in 2020, having previously been a Postdoctoral Research Fellow and Associate Lecturer at UQ.