Speakers

Christina 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 PhD 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, where she is currently a Member of the Computational and Systems Biology Program.  

Dr. Leslie is well known for developing machine learning approaches for the analysis and interpretation of high-throughput biological data – in particular, bulk and single-cell transcriptomic, epigenomic, and 3D genomic sequencing data sets – with the goal of decoding gene regulation.  Biological application domains include basic and cancer immunology, cancer epigenetics, and stem cell biology and cellular differentiation. She is PI, together with Alexander Rudensky, of the NCI U54 Center for Tumor-Immune System for Systems Biology at MSKCC.  She is also co-leads projects in the NHGRI Impact of Genomic Variation on Function (IGVF) consortium and the NIH Common Fund 4D Nucleome (4DN) consortium.