Research

My research interests include:

Development of new computational tools applied to single-cell data

Single-cell transcriptomics has provided an unprecedented resolution of the molecular events underlying different biological processes in development and disease. While many single-cell studies have uncovered cell state heterogeneity and biological trajectories, there is still a lot of untapped potential in these complex datasets. Thus, we are interested in developing new computational tools to further explore and exploit the richness of single-cell data, promoting the reuse of existing data and better interpretation of new datasets. This involves the development of new tools to allow for (i) easier access / visualisation of complex single-cell datasets, (ii) application of machine learning techniques to integrate multiple datasets and interrogate the underlying gene regulation and (iii) deployment of generative models to predict how single cells respond to external stimuli.

Prioritisation of drugs to reverse dysregulated gene regulatory programs

There exist extensive databases cataloging the transcriptional response to drug treatment and an increasing number of studies profiling cells undergoing both genetic and drug perturbations at a single-cell level. Here, we are interested in using statistical and machine learning approaches to link these perturbation profiles to gene regulatory programs that are dysregulated during disease. This allows us to identify potential drugs to reverse disease progression and elucidate the underlying mechanism of action of these drugs.

Application to stem cell biology, neural models, and hematological malignancies

It is crucial to bridge computational and experimental expertise to ensure that the computational models describe biology realistically and predictions can be readily tested in the lab. Specifically, we are interested in applying our computational modeling and analysis to three distinct yet related areas. This includes (i) stem cell biology where cell fate is shown to be highly plastic and cells can be reprogrammed into other lineages by manipulating a small number of regulator genes, (ii) modeling of neurodegenerative disorders and brain development and using stem cell-based neural models and (iii) understanding cell fate transitions in hematological malignancies where genetic mutations and microenvironment often lead to cell fate biases.