Djeane Debora Onthoni, Ph.D., is a researcher specializing in Health Data and Artificial Intelligence. She earned her Ph.D. from Department of Computer Science and Information Engineering, Chang Gung University, Taiwan, where her research focused on computer vision tasks using medical imaging, such as Contrast-enhanced and Non-contrast Computed Tomography (CT) scans. She has experience applying AI methods - including Machine Learning and Deep Learning - for complex disease prediction and disease subtype clustering by analyzing multimodal data from Electronic Health Record (EHR), Genomic data (e.g., Polygenic Risk Score (PRS)), and Imaging data (e.g., Carotid Ultrasound). Her research spans precision medicine applications using resources such as the UK Biobank and Taiwan Biobank during her postdoctoral fellowship at the Institute of Population Health Sciences, National Health Research Institutes, Taiwan. Currently, she is a research fellow at the Institute of Computer Science, Univerisity of Tartu, where her work focuses on applying AI and statistical methods to analyze EHR and genomic data - including PRS from the Estonian Biobank - using the OMOP Common Data Model (CDM) and integrative data approaches.