Mātai Team

Dr. William Schierding is Vision Research Foundation Senior Research Fellow, Ophthalmology, University of Auckland and an Honorary Senior Research Fellow and Principal Investigator (UK Biobank Analysis and Biostatitics Support) at Mātai.

Mātai was established to understand how the brain changes across the lifespan in response to injury, disease, and environmental stressors (and how recovery can be supported before irreversible damage occurs). William’s research aligns closely with this mission by focusing on early biological signals of brain and visual system vulnerability, long before clinical symptoms emerge.

His work integrates genetics, epigenetics, brain and retinal imaging, and artificial intelligence to identify individuals at increased risk of long‑term neurological and visual decline. Rather than treating conditions such as glaucoma, traumatic brain injury, or multiple sclerosis as isolated phenotypes, his research frames them as interacting neurodegenerative and neuroinflammatory processes that unfold over decades.

Much of modern genetics focuses on identifying statistical associations in DNA, such as single nucleotide polymorphisms (SNPs), without explaining the underlying mechanisms of how those signals actually lead to disease. This gap between genetic data and clinical decision-making remains one of the major challenges in precision medicine.

William’s research career addresses this challenge directly. His work brings together genomics, systems biology, and artificial intelligence/machine learning to translate complex molecular data into insights that are meaningful for patients and clinicians. By integrating large-scale biological data with machine learning, his research aims to move medicine beyond risk correlations toward actionable understanding.

His current research focus is ophthalmology, particularly the early detection of eye diseases such as glaucoma. Using large international cohorts, William studies both genetic and clinical biomarkers that signal disease risk long before symptoms appear. His work combines polygenic risk scores, epigenetic markers, medical imaging, and clinical measurements to build predictive models that support earlier diagnosis and more personalized intervention strategies.

William collaborates widely across New Zealand, Australia, the United Kingdom, the United States, and Finland. He is also deeply committed to mentoring students who are interested in combining biology, data science, and machine learning to tackle complex problems in human health.