Summary
Ping-han Hsieh is an Assistant Professor and machine learning scientist with a PhD in Informatics from the University of Oslo and a decade of experience translating deep generative models and multi-omics research into practical biotech solutions. Their doctoral work developed hierarchical variational autoencoders and graph-based approaches that embed prior biological knowledge to improve interpretation and efficiency on single-cell multi-omics and regulatory networks. In industry roles across Tokyo and Taiwan they built user-friendly analytics platforms, automated CI/CD pipelines, and a validated LDT for early PDAC detection that improved sensitivity by 16%, showing an uncommon ability to take models from research to regulated deployment. They combine full-stack development experience (Angular, MongoDB, Express) with advanced ML for heterogeneous graphs and interpretable attribution, enabling non-technical stakeholders to access complex analyses. Based in Oslo with strong ties to Taiwan, Ping-han brings a rare blend of academic rigor, product-focused engineering, and domain expertise in personalized medicine.
10 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Department of Informatics, Doctor of Philosophy - PhD, Department of Informatics at Universitetet i Oslo (UiO)
Master's degree, Graduate Institute of Biomedical Electronics and Bioinformatics, Master's degree, Graduate Institute of Biomedical Electronics and Bioinformatics at National Taiwan Univesity
Chinese, English, Japanese