The cell is a highly mysterious intelligent machine that fascinates my imagination and arouses my curiosity. My research revolves around development and application of principled machine learning methods for interpreting complex biological data sets. I love to design novel machine learning algorithms, especially when they allow the analysis of data in ways that have not yet been possible. I am interested in working on fundamental biological problems that would have an impact on our understanding of human cells and disease mechanisms. An ongoing theme in my research is the integration of multi-omics data sets to understand underlying biological processes that contribute to a specific phenotype. In particular, I work on methods to integrate and analyze high-throuput omics data.
Single cell genomics
Single-cell multi-omics data provides us an opportunity to understand biological processes at the fundamental level. These technologies drive new discoveries and are rapidly advancing, which calls for the constant design and improvement of computational methods for integration, interpretation, and analysis. I would like to develop new methods for single cell data analysis using a combination of rigorous statistical approaches. These methods are necessary to cope with the inherent noise of these technologies and to focus on computationally efficient design and implementation such that resulting method can handle thousands of single cell omics data.
I would like to continue my work on understanding the system perspective of transcriptional and post-transcriptional gene regulation, answering questions like: What are the rules of enhancer gene targeting? How do structural variations in enhancers effect target gene regulation? Which epigenetic signatures contribute to establishing cell identity? Which regulatory interactions are involved in changing cell identity?