Dr. Christina Leslie

  • What are your current research interests?

I develop computational methods, especially machine learning methods, on genomics data sets to study the global regulation of gene expression, and to understand how gene regulatory programs go awry in diseases like cancer.  This area is sometimes called “regulatory genomics” — short for “the genomics of gene regulation”.  Our main application domains are immunology (including cancer immunology), cancer biology, and stem cell biology and cellular differentiation. 

  • What is Computational Biology and how does it relate to your research interest?

Broadly speaking, computational biology is the application of computational and quantitative techniques to biological data.  Both the methods and the applications areas are very broad.  Different researchers in the field use everything from data science/AI approaches to classical mathematical modeling with e.g. ordinary differentiation equations.  Computational biology spans all areas are biological and multiple scales of modeling — from neuroscience to molecular biology to population genetics, from predicting protein structure to inferring gene regulatory networks to classifying pathology images.  I am definitely on the mechanistic side and try to understand how cells function and interaction with each other, and I also use data-driven approaches from machine learning/AI. 

  • How does your research topic relate to current events?

We have recently done quite a bit of work using deep learning models, which have recently been popularized with ChatGPT and Dall-E.  Our biological applications are also connected to topics that the general public might know about — for example, our studies of epigenetic states of T cells and our single-cell studies of the tumor-immune microenvironment can help to explain the situations in which cancer immunotherapy will work in a patient, and when it will fail to work.

  • What research methods does your lab use?

We use machine learning and statistical methods with diverse kinds of genomics data, especially single-cell transcriptomics and chromatin accessibility data, 3D genomics data that tells us about 3D chromatin interactions, spatial transriptomics data, and diverse epigenomic sequencing assays.


  • Have you involved your students in your research?

Of course!  All students who join or intern in my lab are involved in research projects.  We have even had high school students make significant contributions to research papers and projects.

  • Do you have major professional achievements that you want to share with us?

My 20th PhD student is about to defend her dissertation!  I am happy to say that all my former PhD students and postdocs are still involved in science, through research in computational biology or data science in both academia and industry.

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