Uncovering temporal dynamics of large collection of cells by applying machine learning and dynamical systems framework to single cell sequencing data.
I’m a computational scientist working at the intersection of genomics, machine learning and nonlinear dynamics in advancing data-driven scientific discovery.
I earned my PhD in physics focusing on the mathematical modeling of dynamical systems (deterministic and stochastic differential equations) on graphs (complex network topologies) to study the emergence of rich spatio-temporal patterns in various complex systems spanning from physics to ecology. After PhD, I did my first postdoc in theoretical ecology group at the Institute for chemistry and biology of marine ecosystems in Germany, where I worked on developing mathematical models of biodiversity in multi-species ecosystems competing for limited resources and developed expertise in applying machine learning methods to learn species-species trait relationships. Subsequently, I took a second postdoc to delve deeper into exploring some fundamental aspects of scientific machine learning from the lens of complex systems gaining insights that would enhance the interpretability and explainability of ML models.
At JAX, I'm excited to work on predicting cell fate dynamics by constructing a transcriptome-wide continuous vector field from RNA velocity.