Area of research
Artificial Intelligence · Public Health, Environmental and Occupational Health
Research interest
Research interests include Machine Learning in Healthcare, Privacy-Preserving Technologies in Data, Reproductive Biology and Fertility, and Sepsis Diagnosis and Treatment.
Multicenter target trial emulation to evaluate corticosteroids for sepsis stratified by predicted organ dysfunction trajectory
Federated target trial emulation using distributed observational data for treatment effect estimation
Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation
Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging
High-speed optical imaging with sCMOS pixel reassignment
Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care
Biomedical discovery through the integrative biomedical knowledge hub (iBKH)
An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals
A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study