Research

Research overview

My research areas are computational biology and bioinformatics, where I am interested in solving mathematical and computational problems arising from the analysis of functional genomics data.

Causal model selection tests conditional independencies implied by multiple candidate models to select the model that best fits the data. Adapted from Michoel and Zhang (2023)

Workflow of a study combining functional genomics data generation, computational analysis, and experimental validation. From Talukdar et al. (2016)

I am particularly interested in causal inference and scientific machine learning, modelling approaches where existing knowledge about a system is encoded in a qualitative causal diagram or a set of stochastic differential equations, and machine learning and large data are used to “fill the gaps”, that is, to parametrize the model and perform statistical inference.

In biology, my main interest is in systems biology, where I try to understand how genetic variation between individuals influences variation in gene regulation, gene regulatory networks, and health and disease outcomes.

Topics I am particulary interested in at the moment are:

• Causal gene regulatory network inference
• Causal inference for stochastic processes
• Causal models for cellular differentiation and gene expression dynamics
• Causal models for dimensionality reduction
• Branching processes and stochastic processes on trees
• Geometric deep learning

Projects

More established lines of research than the topics above are carried out in the context of externally funded projects, which support most of the people in my group.