About

Current Position:  Nanyang Assistant Professor, NTU, Singapore 

Email Address: hyyeh@ntu.edu.sg

Google Scholar

https://www.researchgate.net/profile/Ivy-Hui-Yuan-Yeh-yehuiyuan

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 deep learning models, including Convolutional Neural Networks (CNNs), to classify archaeological artifact fragments and to perform image restoration using Generative Adversarial Networks (GANs). Preliminary results have been obtained using a Residual Neural Network (ResNet) architecture trained on images of artifacts and achieved a high accuracy (96.0%) in classifying the dynasty. Insight was gained using explainable AI techniques such as GRAD-CAM which showed the model is focusing on the shape of the artifact and motifs. The research findings are currently under review in a peer-reviewed journal. The next phase of the project will incorporate auction house price prediction.

3. Deep Learning Algorithms in Forensic Anthropology: 

Ivy uses machine learning to support forensic medicine doctors in identifying unknown victims. We are currently collaborating with a Taiwanese forensic team, and you’re very welcome to join us if you’re interested. Beyond modern forensics, this approach also helps correct biases in historical and archaeological datasets—by refining age, sex, and population estimates, it improves the accuracy of ancient population reconstruction and the interpretation of past demographic structures.
 
 
Geographic Distribution of Ivy’s International Collaboration Network