Biography

Ivy was trained in the Department of Anthropology and the School of Medicine at National Taiwan University. After working at Academia Sinica in Taipei, she pursued her PhD in the Division of Biological Anthropology at the University of Cambridge. Her research expertise lies in biological anthropology, supported by an interdisciplinary background. For two years, Ivy supervised and mentored students in both laboratory work and essay writing at Cambridge. 

Throughout her career, Ivy has held several roles, including Coordinator of the Medical Humanities Research Cluster at NTU, NTU iGave Ambassador of the School of Humanities, member of the International Advisory Committee (IAC) of the Chinese Heritage Centre (CHC) at NTU, Preparation Committee member for the Conference on Pan-Pacific Anthropocene (ConPPA), editorial board member of the International Journal of Osteoarchaeology, and Topic Editor for Frontiers in Genetics.

Ivy currently has three main projects below: 

I. Bioanthropology and Bioarchaeology Projects:

Ivy is particularly interested in analyzing the health, disease, diet, and nutrition of human communities, as well as patterns of population interaction. Her work focuses especially on the global spread of disease from an evolutionary perspective. Her research projects investigate how diseases have been transmitted through population interactions and migrations across human history, aiming to understand their impact on human health. Pathogens co-evolve with humans and have been transmitted among regional populations, influencing the health of various societies. Studying pathogens across historical and geographical contexts helps illuminate how humanity has been shaped by disease—and what this may mean for the future. 

2. Artificial Intelligence in Archaeological & Museum Analysis:

Ivy is currently training a deep learning method called the Convolutional Neural Network (CNN) to classify artifact fragments and perform image restoration of archaeological fragments using Generative Adversarial Network. Preliminary results have been obtained using a Residual Neural Network (ResNet) within a Convolutional Neural Network (CNN). The CNN was trained with images of artifacts, achieving a high accuracy of 96.0% in identifying background information such as dynasties. In addition, the model was able to predict the contemporary preservation and locations of the artifacts. The research results are currently being written up and planned for publication in relevant journals.

3. Machine Learning Algorithms in Forensic Anthropology: 

Ivy uses machine learning to help forensic medicine doctors identify victims. We are currently working with a Taiwanese forensic team, and you’re welcome to join us if you’re interested.