Long Research Group

Research Areas

Methodology

• Robust statistical and machine learning 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
• Data privacy: federated learning; differential privacy
• Algorithmic fairness
• Foundational AI models such as large language models (LLMs) with application to medicine
• Bayesian methods and modeling
• Clinical trials

Subject-Matter Areas

• cancer
• cardiovascular diseases
• neurological diseases and neurodegeneration
• diabetes
• kidney diseases
• mental health

Funded Research Projects

• 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)