The Su Lab is particularly interested in machine learning at the interfaces between areas including:

  • Real-world data such as electronic health records and American Community Survey data
  • Clinical research data such as randomized controlled trials data.
  • Multi-omics data such as single cell sequencing data and radiomics data.
We are spearheading the paradigm shift of translational research from the “from bench side to bedside” model to the “from real-world data to real-world outcomes” model. We are closely working with clinical and industrial collaborators on pragmatic trials for improving geo-socioeconomic health equity of underrepresented populations during aging and for implementing precision oncology in immune therapies. 

Areas of Research Interests

  • Clinical informatics and bioinformatics, electronic health records, pragmatic trials, geo-economical health disparity, single-cell sequencing, systems biology. 
  • Graph algorithms and artificial intelligence 
  • Chronical complex diseases during aging including
    • Alzheimer’s diseases and cognitive health
    • Cancers and immune therapies
    • Chronic kidney disease
    • Diabetes
    • Physical disabilities. 

Highlighted Research Projects

  • EMR-based pragmatic recruitment for the U.S. POINTER Alzheimer’s Disease clinical trial (Alzheimer’s Association). 
  • Data Coordinating Center for the U.S. POINTER Neuron-imaging Ancillary Study: a radiomics study (NIA/NIA). 
  • Artificial intelligence to redefine cancers in genome-phenome spaces through integrative usage of electronic health records and genomic data (AstraZeneca). 
  • Precision oncology for brain metastasis through incorporating multi-omics, single cell sequencing, radiomics, and clinical outcome data. 
  • EMR-based pragmatic clinical trial: D-CARE (PCORI).