† denotes co-first authorship.

ALICE: A Multifaceted Evaluation Framework of Large Audio-Language Models’ In-Context Learning Ability

Yen-Ting Piao†, Jay Chiehen Liao†, Wei-Tang Chien†, Toshiki Ogimoto†, Shang-Tse Chen, Yun-Nung Chen, Chun-Yi Lee, Shao-Yuan Lo. arXiv preprint, 2026. [arXiv]

  • A three-stage framework that progressively reduces textual guidance to evaluate LALMs’ in-context learning under audio conditioning.
  • Built the LALM inference framework; ran experiments; led results analysis.

TabGSL: Graph Structure Learning for Tabular Data Prediction

Jay Chiehen Liao, Cheng-Te Li. IEEE International Conference on Big Data (BigData), 2025. [IEEE Xplore] · [arXiv]

  • A contrastive-learning GNN with a transformer-based extractor that jointly learns instance correlations and feature interactions for tabular prediction.
  • Outlined a hyperparameter-tuning guide for graph structure learning.

Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions

Cheng-Te Li, Yu-Che Tsai, Chih-Yao Chen, Jay Chiehen Liao. ACM Computing Surveys, 2025. [ACM] · [arXiv] · [GitHub]

  • Systematic review of GNNs designed and implemented for tabular data learning.
  • Presented as tutorials at IEEE ICDE 2023 and The Web Conference (WWW) 2023.

Enhancing PrEP Adherence Through Person-Centred Mobile App Interventions: A Real-World Data and Machine Learning Approach Using UPrEPU Among Gay, Bisexual and Other Men Who Have Sex with Men in Taiwan

Jay Chiehen Liao, Huei-Jiuan Wu, Tsan-Tse Chuang, Tsai-Wei Chen, Carol Strong. Journal of the International AIDS Society, 28(S5), 2025. [paper]

  • Leveraged real-world mobile-app logs and user attributes to predict PrEP-protected sexual events among GBMSM, with SHAP-based analysis of adherence factors.

Predictive Modeling for 14-Day Unplanned Hospital Readmission Risk by Using Machine Learning Algorithms

Yu-Tai Lo†, Jay Chiehen Liao†, Mei-Hua Chen, Chia-Ming Chang, Cheng-Te Li. BMC Medical Informatics and Decision Making, 21, 2021. [paper]

  • An explainable ML model for 14-day unplanned hospital-readmission risk from EHR data of 24k+ patients, with SHAP-based feature analysis aligned with clinical experience.