Christina S. Leslie, Ph.D.

Memorial Sloan Kettering Cancer Center
New York, NY

Dr. Christina S. Leslie is a computational biologist at Memorial Sloan Kettering Cancer Center.

Dr. Leslie did her undergraduate degree in pure and applied mathematics at the University of Waterloo in Canada. She was awarded an NSERC 1967 Science and Engineering Fellowship for graduate study and did a Ph.D. in mathematics at the University of California, Berkeley, where her thesis work dealt with differential geometry and representation theory. She won an NSERC Postdoctoral Fellowship and did her postdoctoral training in the Mathematics Department at Columbia University in 1999-2000. She then joined the faculty of the Computer Science Department and later the Center for Computational Learning Systems at Columbia University, where she began to work in computational biology and machine learning and became the principal investigator leading the Computational Biology Group. In 2007, she moved her lab to Memorial Sloan Kettering Cancer Center (MSKCC), where she is currently a Member of the Computational and Systems Biology Program.

Dr. Leslie is well-known for developing machine learning approaches—algorithms for learning predictive models from data—for the analysis and interpretation of high-throughput biological data, especially from next-generation sequencing. Her research group uses machine learning and other computational methods to study transcriptional and post-transcriptional gene regulatory mechanisms, epigenetic programs governing cell fate decisions in differentiation, and the dysregulation of gene expression programs in cancer. A strong focus area is the analysis of differentiation programs in the immune system and dysfunctional immune cell states in cancer.

Dr. Leslie is Principal Investigator, together with Dr. Alexander Rudensky, of an NCI U54 Center for Cancer Systems Immunology at MSKCC, which studies the role of tumor-immune interactions in cancer and response to cancer immunotherapy. She is also co-chair of the Analysis Working Group of the ENCODE 4.0 project, where she leads a computational project to incorporate data on 3D genomic architecture into epigenomic and gene regulatory models. She is also Chair of the National Institutes of Health (NIH) GCAT panel, which reviews many of the genomics and computational grant applications submitted to NIH.

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