projects

Research

My research fall under two main categories:

1. Machine Learning with limited supervision
   • A review of knowledge-informed machine learning for cancer applications (website)
   • Predicting Tumor Cell Density maps for brain cancer using MRI (Mayo Clinic) (paper)
   • Reconstruction of accelerated MRI using self-supervised learning
   • Automated segmentation and classification of dental lesion from 3D CBCT (Upenn Dental)
   • Segmentation of retinal layers from OCT Images with uncertainty quantification (Feola Lab)
2. Multi-modal learning
   • Prediction of recovery from post-traumatic headache using clinical and imaging data (Mayo Clinic) (paper) (paper)
   • Early prognosis of Alzheimer’s Disease using incomplete multi-modal neuroimaging and genetics data (abstract)

I also analyzed population-level data for public health applications:

1. Large-scale data mining and predictive modeling
   • Prediction of unplanned hospitalizations for Medicare patients (CMS AI Challenge)(paper)
   • Analyzing the public influence of health organizations on Twitter (paper)
   • Analyzing public health policies for sodium reduction in the U.S. (interactive dashboard)

Fun

Outside research, I like to create AI/ML solutions to automatically analyze data. Here are some favorites:

• MMTrip, Your Personal Multi-Modal Planner (website under development)
• AskMendel, a LLM chatbot for automatic bioinformatics data analysis and visualization