Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

sihao xiao

sihao xiao

Sihao Xiao

MSc


DPhil Student

Based at the Big Data Institute, Sihao joined Professor Cornelia Van Duijn’s group in 2021 as a full-time DPhil student. His project focuses on developing Alzheimer’s disease early diagnosis models and identifying potential therapeutic targets. With data from UK Biobank and other in-house cohorts, Sihao will use novel approaches based on machine learning methods to link brain imaging data with multi-omics data and investigate complex patterns within high-dimension data. 

Having completed Imperials’ MSc in Genomic Medicine in 2019, Sihao, as a GeCIP member who has early access to the UK 100K Genome Project, still plays an important role in utilising the power of whole-genome sequencing data to investigate novel pathways of respiratory rare diseases. He is now co-leading the Pan-rare disease Reanalysis Subdomain within the UK 100K Genome Project. Sihao also possesses rich industry experience from his work at the BGI genomics Inc, the largest genome research company in Asia. At BGI, Sihao led and completed several projects spanning different topics including pan-genomes, pharmacogenomics, chronic diseases and cancer early diagnosis. With machine learning methods he developed industry-leading models for accurate cancer diagnosis and tissue-of-origin identification. Recognising his excellent research work, Sihao was selected to the BGI’s management training programme where he reported to board members regularly. He has published papers in high-impacted peer-reviewed journals and several software patents. 

His research foci include identification of new pathologic pathways via large cohort projects, potential therapeutic targets for diseases and easy-accessible early diagnosis and prognosis biomarkers.