Dr Pengyao Ping

Pengyao is a Research Fellow at the Centre for Big Data Research in Health, UNSW Sydney. He holds a PhD in Analytics from the University of Technology Sydney (UTS) and has multidisciplinary research experience in computational biology, statistical genetics, bioinformatics, biomedical data science, machine learning, and artificial intelligence (AI) for healthcare.

His research focuses on developing and applying computational, statistical, machine-learning, and deep-learning methods to integrate complex biomedical and digital health data and improve understanding of disease risk, clinical outcomes, and treatment effects. His experience spans genomics, proteomics, multi-omics, polygenic risk analysis, and predictive modelling using large-scale health data. He has analysed longitudinal electronic health records (EHRs), genomic data, and proteomic data from resources including the All of Us Research Program and UK Biobank to investigate associations among genetic factors, circulating proteins, clinical characteristics, medication use, and mental health.

His current research focuses on cardiovascular risk prediction and treatment evaluation across populations with and without type 2 diabetes and mental health conditions. This work uses routinely collected EHRs and longitudinal health information to develop and assess approaches for identifying risk and understanding how treatment effects may vary between individuals. His broader interests include AI for clinical and biomedical research, natural language processing (NLP) of clinical notes, model interpretability, and translating data-driven methods into useful clinical decision-support tools. He also develops scalable computational methods for bioinformatics, including graph-based approaches to biological sequence analysis.

Through his interdisciplinary research integrating complex biological and clinical data, Dr Ping aims to advance precision medicine and translate data-driven discoveries into meaningful outcomes with real-world impact in disease prediction, prevention, and care.