Aarthi Venkat
Eric and Wendy Schmidt Center Postdoctoral Fellow @ Broad Institute
Eric and Wendy Schmidt Center
Yale University
As the scale and scope of biomedical data exponentially increase, unsupervised machine learning is becoming a crucial step toward scientific discovery. During my Ph.D. at Yale University in the Krishnaswamy Lab, I developed representation learning approaches that learn from geometric structure and reveal patterns that characterize cellular and molecular behavior, especially from single-cell sequencing data. Currently, I am a Postdoctoral Fellow at the Broad Institute to extend these ideas beyond single-cell resolution, bridging knowledge, geometry, and network biology across multiple scales for an integrated, systems-wide approach to discovery and intervention.
My interests are highly interdisciplinary, and I have led collaborations spanning a broad range of research areas, appearing in Nature, Science Immunology, Cell Patterns, Cell Trends in Immunology, and Genome Research. Additionally, my work has been accepted at notable computer science and graph signal procesing conferences, including SampTA, LoG, GSP, and ICASSP.
Projects
AAnet resolves a continuum of spatially-localized cell states to unveil tumor complexity
AAnet is a neural network for nonlinear archetypal analysis of single-cell data.
A Venkat*, S Youlten*,…,S Krishnaswamy, CL Chaffer. Cancer Discovery In Revision.
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.
Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis
DSAE employs a directed version of a geometric scattering transform for embedding nodes of directed graphs.
A Venkat*, J Chew*, F Cardoso Rodriguez, CJ Tape, M Perlmutter, S Krishnaswamy. ICASSP 2024.
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.
Mapping the gene space at single-cell resolution with gene signal pattern analysis
GSPA learns gene-gene relationships from single-cell data by embedding the gene space.
A Venkat, S Leone, S Youlten, E Fagerberg, J Attanasio, NS Joshi, S Krishnaswamy. Nature Computational Science In Revision.
Multiscale geometric and topological analyses for characterizing and predicting immune responses from single cell data
This review describes approaches to characterize immune heterogeneity at multiple resolutions.
A Venkat, D Bhaskar, S Krishnaswamy. Cell Trends in Immunology.
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 regulatory networks driving cancer cell state plasticity with neural ODE-based optimal transport
TrajectoryNet learns continuous and cell-specific trajectories from longitudinal single-cell data.
A Tong*, M Kuchroo*, S Gupta, A Venkat,…,CL Chaffer, S Krishnaswamy. Nature In Review.
Single-cell analysis reveals transcriptional dynamics in primary parathyroid tissue
The first parathyroid single-cell atlas of primary parathyroid tissue.
A Venkat*, M Carlino*, B Lawton*,…,S Krishnaswamy, D Krause. Genome Research.
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.
The cellular and molecular mechanisms governing β cell-driven pancreatic adenocarcinoma
Disentangling the origin of β cells misexpressing pro-tumorigenic hormones in the context of obesity and other stressors.
C Garcia*, A Venkat*,…,S Krishnaswamy, MD Muzumdar. In Preparation.