activity
20132026
most citedSensor Selection for Estimation with Correlated Measurement Noise

198 citations · 445 across the 40 of their papers we have counts for

collaborators

53 papers

eess.SP2026

Conformal Risk Control for Multiuser Beamforming

Aman Goel, Sundeep Prabhakar Chepuri

We consider multiuser downlink beamforming with prescribed signal-to-interference-plus-noise ratio (SINR) requirements under imperfect channel state information and a tight transmi…

stat.ML2026

Signal Processing over Product DAGs: Causal Shifts and Filters

Sundeep Prabhakar Chepuri, Antonio G. Marques, Maulik Devmurari +1

We develop a signal processing framework for signals indexed by the product of two directed acyclic graphs (DAGs) and described by a linear structural equation model (SEM). Such a…

cs.LG2026

Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models

Ayushman Raghuvanshi, Thummaluru Siddartha Reddy, Sundeep Prabhakar Chepuri +1

Continuous-time dynamic graphs (CTDGs) provide a richer framework to capture fine-grained temporal patterns in evolving relational data. Long-range information propagation is a key…

cs.LG2025

Task-driven Heterophilic Graph Structure Learning

Ayushman Raghuvanshi, Gonzalo Mateos, Sundeep Prabhakar Chepuri

Graph neural networks (GNNs) often struggle to learn discriminative node representations for heterophilic graphs, where connected nodes tend to have dissimilar labels and feature s…

cs.LG2025

Covariance Scattering Transforms

Andrea Cavallo, Ayushman Raghuvanshi, Sundeep Prabhakar Chepuri +1

Machine learning and data processing techniques relying on covariance information are widespread as they identify meaningful patterns in unsupervised and unlabeled settings. As a p…

cs.LG2025

Conformal Inference for Time Series over Graphs

Sonakshi Dua, Gonzalo Mateos, Sundeep Prabhakar Chepuri

Trustworthy decision making in networked, dynamic environments calls for innovative uncertainty quantification substrates in predictive models for graph time series. Existing confo…