Young researchers at the University of Hong Kong (HKU) have achieved outstanding results in the Excellent Young Scientists Fund (Hong Kong and Macau) for 2022.

Ten HKU young scientists have been awarded the prestigious fund under the National Natural Science Foundation of China, an organisation managed by the Ministry of Science and Technology (MOST).

This has been the fourth consecutive year for HKU to be awarded the highest number of projects among its peer institutions, after the fund was extended to Hong Kong and Macau for applications by eight designated universities since 2019.

The Excellent Young Scientists Fund is granted annually to support young male scientists under age 38 and young female scientists under age 40 who have attained outstanding achievements in research, to further expand in areas of their own choice.

It is highly competitive, with only 25 projects in total funded across Hong Kong and Macau this year. Each project will receive funding of RMB2 million over a maximum period of three years, in the form of cross-border remittance to directly support the researchers' work in Hong Kong or Macau.

Congratulations to Dr Yuanhua Huang from our school who has been awarded the China's Excellent Young Scientists Fund 2022 and his award winning project is:

Dr Yuanhua Huang
Assistant Professor, School of Biomedical Sciences, LKS Faculty of Medicine (joint appointment with Department of Statistics and Actuarial Science, Faculty of Science)
Project Title: Single-cell data science
Dr Huang’s main research direction is single-cell data science in the field of bioinformatics, through the development of data science methods for effective analysis and knowledge discovery of single-cell omics data. He has developed a series of Bayesian models and machine learning approaches in this research direction and addressed several important problems for the analysis of single-cell genomic and transcriptomic data. Dr Huang’s project intends to develop integrative and interpretable machine learning methods to tackle the challenges in single-cell data science, with a focus on analysing the dynamic changes of cells.