Velocity-assisted multidimensional scaling. - Sandeep Kumar 005; Ketan Rajawat (SPAWC, 2015)

Distributed interference alignment for MIMO cellular network via consensus ADMM. - Sandeep Kumar 005; Ketan Rajawat (GlobalSIP, 2016)

Cooperative Localization of Mobile Networks Via Velocity-Assisted Multidimensional Scaling. - Sandeep Kumar 005; Raju Kumar; Ketan Rajawat (IEEE Trans. Signal Process., 2016)

Distributed asynchronous localization over WSNs via non-convex consensus ADMM. - Sandeep Kumar 005; Ketan Rajawat (NCC, 2017)

Asynchronous Optimization Over Heterogeneous Networks Via Consensus ADMM. - Sandeep Kumar 005; Rahul Jain 009; Ketan Rajawat (IEEE Trans. Signal Inf. Process. over Networks, 2017)

Stochastic Multidimensional Scaling. - Ketan Rajawat; Sandeep Kumar 005 (IEEE Trans. Signal Inf. Process. over Networks, 2017)

Parameter Estimation of Heavy-Tailed Random Walk Model from Incomplete Data. - Junyan Liu; Sandeep Kumar 005; Daniel P. Palomar (ICASSP, 2018)

Bipartite Structured Gaussian Graphical Modeling via Adjacency Spectral Priors. - Sandeep Kumar 005; Jiaxi Ying; José Vinícius de Miranda Cardoso; Daniel P. Palomar (ACSSC, 2019)

Robust Factor Analysis Parameter Estimation. - Rui Zhou; Junyan Liu; Sandeep Kumar 005; Daniel P. Palomar (EUROCAST (2), 2019)

Parameter Estimation of Heavy-Tailed AR(p) Model from Incomplete Data. - Junyan Liu; Sandeep Kumar 005; Daniel P. Palomar (EUSIPCO, 2019)

Structured Graph Learning Via Laplacian Spectral Constraints. - Sandeep Kumar 005; Jiaxi Ying; José Vinícius de Miranda Cardoso; Daniel P. Palomar (NeurIPS, 2019)

Distributed Inexact Successive Convex Approximation ADMM: Analysis-Part I. - Sandeep Kumar 005; Ketan Rajawat; Daniel P. Palomar (CoRR, 2019)

Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM. - Junyan Liu; Sandeep Kumar 005; Daniel P. Palomar (IEEE Trans. Signal Process., 2019)

Optimization Algorithms for Graph Laplacian Estimation via ADMM and MM. - Licheng Zhao; Yiwei Wang 001; Sandeep Kumar 005; Daniel P. Palomar (IEEE Trans. Signal Process., 2019)

A Unified Framework for Structured Graph Learning via Spectral Constraints. - Sandeep Kumar 005; Jiaxi Ying; José Vinícius de Miranda Cardoso; Daniel P. Palomar (J. Mach. Learn. Res., 2020)

Student's $t$ VAR Modeling With Missing Data Via Stochastic EM and Gibbs Sampling. - Rui Zhou; Junyan Liu; Sandeep Kumar 005; Daniel P. Palomar (IEEE Trans. Signal Process., 2020)

Parameter Estimation for Student's t VAR Model with Missing Data. - Rui Zhou; Junyan Liu; Sandeep Kumar 005; Daniel P. Palomar (ICASSP, 2021)

Majorization-Minimization on the Stiefel Manifold With Application to Robust Sparse PCA. - Arnaud Breloy; Sandeep Kumar 005; Ying Sun 003; Daniel P. Palomar (IEEE Trans. Signal Process., 2021)

Robust and Globally Sparse Pca via Majorization-Minimization and Variable Splitting. - Hugo Brehier; Arnaud Breloy; Mohammed Nabil El Korso; Sandeep Kumar 005 (ICASSP, 2023)

Estimating Normalized Graph Laplacians in Financial Markets. - José Vinícius de Miranda Cardoso; Jiaxi Ying; Sandeep Kumar 005; Daniel P. Palomar (ICASSP, 2023)

Graph of Circuits with GNN for Exploring the Optimal Design Space. - Aditya Hemant Shahane; Saripilli Swapna Manjiri; Ankesh Jain; Sandeep Kumar 005 (NeurIPS, 2023)

No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation. - Nimesh Agrawal; Anuj Kumar Sirohi; Sandeep Kumar 005; Jayadeva (AAAI, 2024)

Unveiling Graph Structures for Machine Learning: Learning, Structuring, and Coarsening. - Manoj Kumar; Sandeep Kumar 005 (COMAD/CODS, 2024)

HistoGraphCoarse: Strategizing Graph Coarsening Techniques for Efficient Analysis of Gigapixel Histopathological Images. - Ekta Srivastava; Syed Mohammed Danish; Kumar Arjun; Manoj Kumar; Mohit Kataria; Syed Farhan Abbas; Ishaan Gupta; Sandeep Kumar 005 (GRAIL@MICCAI, 2024)

CoRE-BOLD: Cross-Domain Robust and Equitable Ensemble for BOLD Signal Analysis. - Vipul Kumar Singh; Jyotismita Barman; Sandeep Kumar 005; Jayadeva (ML4H@NeurIPS, 2024)

UGC: Universal Graph Coarsening. - Mohit Kataria; Sandeep Kumar 005; Jayadeva (NeurIPS, 2024)

Multi-Component Coarsened Graph Learning for Scaling Graph Machine Learning. - Subhanu Halder; Manoj Kumar; Sandeep Kumar 005 (WWW (Companion Volume), 2025)

A novel coarsened graph learning method for scalable single-cell data analysis. - Mohit Kataria; Ekta Srivastava; Kumar Arjun; Sandeep Kumar 005; Ishaan Gupta; Jayadeva (Comput. Biol. Medicine, 2025)

An Efficient Framework for Epidemiological Parameter Estimation via Graph Reduction and Graph Neural Networks. - Muhammad S. T. Alfas; Manoj Kumar; Shaurya Shriyam; Sandeep Kumar 005 (ACM Trans. Knowl. Discov. Data, 2025)

A Unified Optimization-Based Framework for Certifiably Robust and Fair Graph Neural Networks. - Vipul Kumar Singh; Sandeep Kumar 005; Avadhesh Prasad; Jayadeva (IEEE Trans. Signal Process., 2025)

GOTHAM: Graph Class Incremental Learning Framework under Weak Supervision. - Aditya Hemant Shahane; Prathosh A. P.; Sandeep Kumar 005 (CoRR, 2025)

GraphFLEx: Structure Learning Framework for Large Expanding Graphs. - Mohit Kataria; Nikita Malik; Sandeep Kumar 005; Jayadeva (CoRR, 2025)

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening. - Mohit Kataria; Shreyash Bhilwade; Sandeep Kumar 005; Jayadeva (CoRR, 2025)