Research Aims

1. Integrating Deep Learning and Single-Molecule Sequencing for rapid Multiomics
My research leverages deep learning to decode the complex information captured by single-molecule sequencing technologies. By integrating this flavor of data with deep learning, I aim to build predictive models that can assist both researchers and clinicians in understanding genomic diseases.

2. Single-Molecule Tools to Understand Biophysics of Genome Maintenance, Regulation, and Stability
I focus on developing and applying single-molecule approaches to probe the fundamental mechanisms underlying genome maintenance and regulation. These tools enable direct visualization of chromatin dynamics, DNA-protein interactions, and the structural changes underlying crucial genomic processes. 

3. Uncovering Function and Regulation of Repetitive DNA and the "Dark Genome"
Repetitive DNA and other poorly characterized regions of the genome, often referred to as the "dark genome," represent a vast and under-explored genomic frontier. I work on understanding the roles these elements play in genome organization, gene regulation, and genome stability. I aim to unravel the sequence complexity and understand the layered regulatory mechanisms within these regions.