Our laboratory develops computational methods for genomics and proteomics based on spectral analysis, machine learning, AI, deep learning, signal processing and statistics of high-dimensional data.
We analyze data from a variety of high throughput experiments such as single cell RNA sequencing (scRNA-seq), spatial proteomics, spatial transcriptomics, Exome-seq, ChIP-seq, cytometry, chromosome conformation capture sequencing as well as other multiplexed modalities.
Our methodological development fall into several categories: a) pre-processing tasks such as denoising, removal of batch effects and imputation of missing values, b) scalable algorithms of dimensional reduction techniques for compression and visualization of very large genomics datasets , c) differential analysis tasks for detecting differences across samples with different phenotype/state/condition with the aim of discovering biomarkers, d) bi-clustering and co-organization of large tabulated datasets, e) intra and inter regulation and communication between different cell types, f) signal analysis tools for analyzing data from spatial transcriptomics and proteomics modalities, and g) tree based models in the context of cancer and phylogeny.
Our group includes postdocs, PhD students and MD-PhD students with backgrounds in mathematics, statistics, computer science, and bioinformatics.
Our collaborations span several biomedical areas interrogating the immune system at the single cell level, the molecular profiling of several cancers including skin, kidney, breast and lung tissues, the cellular landscape of brain tissue from donors with HIV and substance use disorders at the single cell level, and the hair follicle development and regeneration.