Research interests lie at the intersection of novel computational innovations and applications in biology and draw upon ideas from probabilistic graphical models, statistical inference, mathematical models, genomics, transcriptomics, evolution and cancer biology. His research is focused on the design of scalable computational techniques backed by probabilistic modeling and statistical inference methods for understanding the biology of cancer and furthering the knowledge about single-cell biology
We are interested in developing probabilistic frameworks that can integrate mutation allele frequency from bulk sequencing and mutation profile from single-cell sequencing for better understanding the heterogeneous subpopulations in cancer tissue and inferring the temporal order in which the somatic mutations were acquired. See here for details.
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