Aarthi Venkat
Postdoctoral Fellow in the Schmidt Center @ Broad Institute of MIT and Harvard
Eric and Wendy Schmidt Center
Yale University
Aarthi Venkat is a Postdoctoral Fellow in the Schmidt Center at the Broad Institute of MIT and Harvard. Co-mentored by Drs. Marinka Zitnik and Nir Hacohen, her work focuses on translating molecular and cellular insights into personalized patient interventions using geometric machine learning. Prior to her postdoctoral work, Aarthi completed her Ph.D. at Yale University with Dr. Smita Krishnaswamy, where she developed new methodology and collaborated across diverse disciplines toward an improved understanding of single-cell biology. Her work has appeared in journals such as Nature, Cancer Discovery, Genome Research, and Cell Patterns, as well as in computer science conferences such as ICASSP, LoG, and GSP.
Selected Projects
A reservoir of stem-like CD8+ T cells in the tumor-draining lymph node preserves the ongoing antitumor immune response
Stem-like CD8+ T cells in dLN maintain stemness and protect antitumor T cells from terminal differentiation in the TME.
KA Connolly, M Kuchroo, A Venkat,…,NS Joshi. Science Immunology 2021.
Graph Fourier MMD for Signals on Graphs
GFMMD embeds signals defined on graphs via an optimal witness function.
S Leone, A Tong, G Huguet, A Venkat, G Wolf, S Krishnaswamy. SampTA 2023.
Inferring dynamic regulatory interaction graphs from time series data with perturbations
RiTINI infers time-varying interaction graphs to predict casual behavior of a system. D Bhaskar, S Magruder, E De Brouwer, A Venkat, F Wenkel, G Wolf, S Krishnaswamy. LoG Conference 2023.
PD-1 maintains CD8 T cell tolerance towards cutaneous neoantigens
PD-1 avoids immunopathology by preventing CD8 T cells to attain fully functional state.
M Damo, N Hornick, A Venkat,…,S Krishnaswamy, N Joshi. Nature 2023.
Revealing dynamic temporal trajectories and underlying regulatory networks with Cflows
Cflows learns continuous and cell-specific trajectories from longitudinal single-cell data.
X Sun*, S Gupta*, A Tong*, M Kuchroo*, D Bhaskar, C Liu, A Venkat,…,CL Chaffer, S Krishnaswamy. In Review.
Single-cell multi-modal GAN reveals spatial patterns in single-cell data from triple-negative breast cancer
scMMGAN uses adversarial learning to integrate single-cell and spatial modalities.
M Amodio, SE Youlten, A Venkat,…,CL Chaffer, S Krishnaswamy. Cell Patterns 2022.