Tsai-Ning Wang

Department of Industrial Design

Eindhoven University of Technology (TU/e)

Eindhoven, The Netherlands

My research focuses on the intersection of Artificial Intelligence and audio, with an emphasis on applications in healthcare. I am particularly interested in how multimodal systems can enhance the way we interact with and interpret medical information.

I hold a master’s degree in Language Technology from Uppsala University, where I developed a strong foundation in natural language processing and machine learning. My current work builds on that background by exploring multimodal systems that can interpret physiological signals in richer clinical context and support more meaningful decision-making in healthcare. 🫁🫀!

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selected publications

CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning

Tsai-Ning Wang, Lin-Lin Chen, Neil Zeghidour, and 1 more author

2025

Accepted at AHLI CHIL 2025

DOI Bib

@misc{wang2025careaqa,
  title = {CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning},
  author = {Wang, Tsai-Ning and Chen, Lin-Lin and Zeghidour, Neil and Saeed, Aaqib},
  year = {2025},
  eprint = {2505.01199},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
  doi = {10.48550/arXiv.2505.01199},
  url = {https://arxiv.org/abs/2505.01199},
  note = {Accepted at AHLI CHIL 2025},
}

Language Models as Semantic Teachers: Post-Training Alignment for Medical Audio Understanding

Tsai-Ning Wang, Lin-Lin Chen, Neil Zeghidour, and 1 more author

2025

DOI Bib

@misc{wang2025semanticteachers,
  title = {Language Models as Semantic Teachers: Post-Training Alignment for Medical Audio Understanding},
  author = {Wang, Tsai-Ning and Chen, Lin-Lin and Zeghidour, Neil and Saeed, Aaqib},
  year = {2025},
  eprint = {2512.04847},
  archiveprefix = {arXiv},
  primaryclass = {cs.SD},
  doi = {10.48550/arXiv.2512.04847},
  url = {https://arxiv.org/abs/2512.04847},
}