Shashwat Kumar
Postdoctoral Fellow, Department of Applied Mathematics and Statistics
Johns Hopkins University
Probabilistic and geometric methods for biological imaging, built to be invariant to nuisance variation such as tissue deformation, cell orientation or camera viewpoint, so that what remains is the biological signal. The work draws on shape analysis, Riemannian geometry, statistics and Bayesian inference.
Current applications: phenotyping cells by linking nuclear shape to gene expression in spatial transcriptomics, tracking individual synapses across days in longitudinal in vivo microscopy, and modeling human pose trajectories for clinical movement assessment. More on the research.
Ph.D. in Systems Engineering, University of Virginia, 2024. Previously a Junior Research Fellow at the Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore.
News
- Nov 2026Speaking in a minisymposium at the SIAM Conference on Mathematics of Data Science (MDS26), Salt Lake City.
- Oct 2026Talk on KSTITCH at the Department of Applied Mathematics and Statistics, Georgetown University.
- Aug 2026Version 2 of KSTITCH, linking cellular morphology and gene expression in spatial transcriptomics, is on bioRxiv.
- May 2026Preprint on Bayesian in vivo synapse tracking with joint Poisson deconvolution and diffeomorphic registration.
- 2025A shape-based functional index for pediatric motor function published in PLOS ONE.