My broad set of research interests center around the application of bio- and cheminformatic methods to biomedical research. If you’re interested in working together or have questions about my research, send me an email.

Context-specific biological networks: My PhD thesis focuses on the application of protein correlation profiling to investigate how the cellular protein-protein interaction network is rewired in biologically relevant contexts. I’ve developed computational methods for network inference from complex proteomic datasets, and applied these to experimental data to map the protein-protein interaction network across mouse tissues or in response to specific cellular stimuli, such as type I interferon. I also have an interest in using bulk and single-cell transcriptomics to infer context-specific biological networks from high-throughput sequencing data.

Spinal cord injury: I have a broad interest in the application of ‘-omics’ techniques to better understand the pathobiology of spinal cord injury, and a particular interest in developing molecular biomarkers of injury severity and recovery. I’ve applied proteomics, bulk and single-cell transcriptomics, metabolomics, and lipidomics to unravel the biology of this complex neurological condition and identify potential pharmacotherapies.

Single-cell genomics: I’m using single-cell transcriptomics to dissect the molecular mechanisms underlying human disease and its treatment at the level of individual cells, with a particular focus on the central nervous system. In parallel, I’m developing computational tools to address key biological and technical gaps in the analysis and interpretation of these experiments.

Deep learning for chemistry: I’m involved in several projects seeking to apply some of the most recent advances in machine and deep learning, especially generative models, to deepen our understanding of the chemical world around and within us.

Natural product discovery: I remain interested in strategies to advance the discovery of bioactive natural molecules by connecting genomic sequence information to the chemical structure of genomically encoded but cryptic metabolites.