Dr. V leads the TDA Data Lab! If you are interested in joining the group, please learn more here.
Yize studies the progenitors of Type Ibn supernovaeāstripped-envelope explosions interacting with helium-rich circumstellar material. He also works on foundation models for supernovae, with a focus on incorporating host galaxy context to improve physical inference.
Anya studies the host galaxies of supernovae and other transients, using large samples to uncover connections between transient types and their progenitor systems.
Sissi leads observational studies of stripped, interacting supernovae.
Kaylee develops machine learning methods to classify supernovae in real time. Most recently, she has used these classification algorithms to understand hydrogen-rich (Type II) core-collapse supernovae.
Ken uses continous time-series models to understand active galactic nuclei.
Anna uses machine learning and radiative transfer simulations to study kilonova populations and the observability of GRB thermal counterparts with LSST. As an undergraduate, she developed methods to detect precursor emission from core-collapse supernovae.
Karthik uses a combination of observational and machine learning methodologies to understand stripped-envelope supernovae. Most recently, he works on new emulation techniques for radiative transfer simulations of stripped-envelope supernovae.
Our beloved lab mascot and good boy! š
We are grateful for the contributions of our former lab members: