Research Projects
Development of deep learning methods for biomedical informatics

Recent advances in artificial intelligence have shown great promise in multiple domains. Similarly, deep learning frameworks, like transformers, have also shown excellent potential in handling various biomedical data and accelerating knowledge extraction from large datasets. In our lab, we are actively working on the development of deep learning methods, leveraging large-scale foundation models, to enhance the analysis of biomedical data, in both predictive and generative manners.

In this broad topic, we welcome applicant(s) to join us either on existing projects or designing new projects to solve different biomedical informatics challenges by deriving and implementing advanced deep learning methods. Potential topics include but are not limited to the following:

1. Employ medical image modeling to enhance reproductive medicine.
2. Fuse histopathology images and spatial transcriptomics to dissect detailed tumour-immune interaction.
3. Develop large sequence model(s) to predict genomic and proteomic properties. 

We are generally looking for candidates with a strong quantitative background in (scientific) computing and statistical inference, and interests in biomedical challenges.

Professor YH Huang, School of Biomedical Sciences

Professor Huang is an assistant professor in the School of Biomedical Sciences and the Department of Statistics and Actuarial Science at the University of Hong Kong (HKU). He was trained in machine learning and bioinformatics at Tsinghua, Edinburgh, and Cambridge universities and the European Bioinformatics Institute (EMBL-EBI). His lab is supported NSFC Excellent Young Scientist Fund and has strong expertise in statistical machine learning for analysing single-cell genomics and broad biomedical data. He is serving as an Editorial Board member for Genome Biology and an Advisory Board member for Patterns.

Biography
HKU Scholars Hub
Lab Homepage
ORCID
yuanhua@hku.hk

For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description.  Interested candidates are advised to enclose with your email:

1. your CV,
2. a brief description of your research interest and experience, and
3. two reference letters (not required for HKUMed UG students seeking MRes[Med]/URIS projects).

Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to rpgmed@hku.hk.

HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.  

HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information.