I work on integrative cross-species data analysis and explainability of deep learning models in genomics.
I am primarily trained as a computational scientist. During my Ph.D., I worked on the developing probabilistic machine learning methodologies for various computational problems in engineering systems (uncertainty quantification, stochastic partial differential equations, dynamical systems, inverse problems etc.). In my first postdoctoral appointment, I worked on the problems pertaining to the interpretability of sequence-function relationships learned by DNN models from high-throughput sequencing functional assay datasets. As a computational scientist at JAX, I work on cross-species integrative analysis of single-cell functional data.