Research Areas
Methodology
- Large language models (LLMs), foundation models, and agentic AI with application to biomedicine
- Robust ML/AI methods for big, complex health data
- Integrative analysis of multi-omics data
- Integrative analysis of imaging-omics data
- Deep learning and representation learning for analysis of electronic health records (EHRs) data
- Knowledge-guided learning methods for analysis of -omics data including deep learning
- Explainable and fair deep learning models for analysis of -omics data and EHRs data
- Missing data methods
- Causal learning
- Responsible AI/ML: federated learning; differential privacy; algorithmic fairness
- Bayesian methods and modeling
- Clinical trials
Subject-Matter Areas
- Cancer
- Cardiovascular diseases
- Neurological diseases and neurodegeneration
- Diabetes
- Kidney diseases
- Mental health
Funded Research Projects (Selected)
- Advancing Neurosymbolic AI for Smart and Trustworthy Healthcare (funded by NIH/NIBIB, joint with Mayur Naik and Ravi Parikh)
- Safe and Explainable AI-enabled Decision Making for Personalized Clinical Decision Support (funded by ARPA-H, in collaboration with colleagues in SEAS and PSOM)
- Robust privacy preserving distributed analysis platform for cancer research: addressing data bias and disparities (funded by NIH/NCI, joint with Dr. Xiaoqian Jiang)
- Advancing the Coordinating Center for the Canine Cancer Immunotherapy Network (funded by NIH/NCI, joint with Dr. Nicola Mason)
- Statistical Modeling of Alzheimer's Disease Progression Integrating Brain Imaging and -Omics Data (funded by NIH/NIA, joint with Dr. Suprateek Kundu)
- Advancing Analysis of Multi-omics Data in Alzheimer's Disease Research (funded by NIH/NIA)
- Privacy-preserving Methods and Tools for Handling Missing Data in Distributed Health Data Networks (funded by NIH/NIGMS)
- Coordinating Center for Canine Immunotherapy Trials and Correlative Studies (funded by NIH/NCI, joint with Dr. Nicola Mason)
- Development and Assessment of Decision Support System for Detection of Kidney Obstruction (funded by NIH)
- Statistical Methods for Handling Missing Data in Large Research Studies (funded by PCORI)
- Advancing Mobile Health using Big Data Analytics: Statistical and Dynamical Systems Modeling of Real-Time Adaptive m-Intervention for Pain Management (funded by NSF)
- Statistical Methods for Causal Inference in Observational Studies (funded by NIH)
- Knowledge-guided Feature Selection for Analysis of Genomic Data (funded by NIH)