My research program focuses on developing and applying artificial intelligence (AI, e.g. novel deep learning tools) and large-scale statistical techniques for integrative analysis of big multimodal health data (omics data, imaging data, administrative and electronic medical records) for precision medicine. My group also collaborates very closely with local, national and international life science scientists and clinicians on different omics projects. Current PhD/MSc students' thesis projects are in the research areas: radiogenomics, medical imaging, rare disease diagnosis using facial photos and phenotypes, drug discovery, microbiome, single cell RNA sequencing and omics data integration.
Software Tools Developed in The Hu Lab
Web: Sparse Matrix Proﬁle DenseNet for COVID-19 Diagnosis
Description: A Two-dimensional Sparse Matrix Proﬁle DenseNet for COVID-19 Diagnosis Using Chest CT Images
Citation: Liu et al. 2020, IEEE Access
Web: DTF: Deep tensor factorization for predicting anticancer drug synergy
Description: A new algorithm integrating tensor factorization and deep learning to predict anticancer drug combinations
Citation: Sun et al. 2020, Bioinformatics
Description: This repository contains three parallel implementation of the LCS algorithm in MPI, OpenMP, and hybrid MPI-OpenMP platforms.
Citation: Shikder et al. 2019, BMC Research Notes.
RecurrentCNV: A graph-based tool to recall recurrent copy number variations