Publication

Journals

1.  A Unified Optimization-Based Framework for Certifiably Robust and Fair Graph Neural Networks, with  Vipul and  Avadesh, IEEE Transactions on Signal Processing, 2024

2.  A Unified Framework for Optimization-Based Graph Coarsening, with: Manoj and Anurag, The Journal of Machine Learning  Research (JMLR), 2023. [Paper]. 

3.  Modularity aided consistent attributed graph clustering via coarsening: with Samarth, Yukti, and Manoj, Transactions on Machine Learning Research (TMLR 2025).  

4. Locality Sensitive Hashing-based Dataset Reduction for Deep Potential Training. Amol, Anuj, Neha, Jaydeva, and Tarak,  J. Chem. Theory Comput. 2025,  

5. GOTHAM: Graph Class Incremental Learning Framework under Weak Supervision, Aditya, Prathosh, Sandeep, Transactions on Machine Learning Research (TMLR 2025).  

6. A Novel Coarsened Graph Learning Method for Scalable Single-Cell Data Analysis, Mohit, Ekta, Ishaan, Jayadeva, Computers in Medicine and Biology, CBME 2025

7. An Efficient Framework for Epidemiological Parameter Estimation via Graph Reduction and Graph Neural Networks, Alfas, Manoj, Shaurya, and Sandeep, ACM Transactions on KDD.

8. Detecting Microstate Transition in Human Brain via Eigenspace of Spatiotemporal Graph, with: Raghav Dev and Tapan Gandhi. IEEE Sensor Letters.

9. Fault Detection of Multi-variate Time Series Data using Weighted Spectral Graph Auto-Encoder Networks, with: Umang Goswami, Hari Prasad, and Prakash Kumar.  Franklin Journal

10. Majorization-Minimization on the Stiefel Manifold with application to Robust Sparse PCA, with: A. Berloy, S. Ying, and D. Palomar, IEEE Transactions on Signal Processing, vol. 69, 2021.  [Paper].

11. Student's-t VAR Modelling with Missing Data via Stochastic EM and Gibbs Sampling,  With: R. Zhou, J. Liu, and D. Palomar,  IEEE Transactions on Signal Processing, Vol. 68,  pp. 6198-6211, 2020 [Paper].

12. A Unified Framework for Structured Graph Learning via Spectral Constraints With: J. Ying, Ze. Vinicius, and D. Palomar , Journal of Machine Learning Research (JMLR), Vol. 21, no. 22, pp. 1-60, January 2020 [Paper],[R Package], [Python Code].

13. Parameter Estimation of Heavy-tailed AR Model with Missing Data via Stochastic EM With: J. Liu, Sandeep Kumar, and D. Palomar, IEEE Transactions on Signal Processing, vol. 67, no. 8, pp.2159-2172, April 2019 [Paper]

14. Optimization Algorithms on Graph Laplacian Estimation via ADMM and MM [Paper] With: L. Zhao, Y. Wang, and D. Palomar, IEEE Transactions on Signal Processing, vol. 67, no. 16, pp. 4231-4244, August 2019

15. Asynchronous Optimization Over Heterogeneous Networks via Consensus ADMM, [Paper] With: R. Jain and K. Rajawat, IEEE Transactions on Signal and Information Processing over Networks, vol. 3, no. 1, pp. 114-129, March 2017.

16. Cooperative Localization of Mobile Networks via Velocity-Assisted Multidimensional Scaling, [Paper] With: R. Kumar and K. Rajawat, IEEE Transactions on Signal Processing, vol. 64, no. 7, pp. 1744-1758, April 1, 2016,

17. Stochastic Multi-Dimensional Scaling, [Paper]. With: K. Rajawat, IEEE Transaction on Signal and Information Processing Over Networks, Special Issue on Distributed Information Processing in Social Networks", vol. 3, no. 2, pp 360-375, Feb 13, 2017   

Conferences

A* Conferences (>= 9 pages)

1. HyperDefender: A Robust Framework for Hyperbolic GNN, with Nikita and Rahul, AAAI 2025

2. Optimization Framework for Semi-supervised Attributed Graph Coarsening, with Manoj, Subhanu, UAI 2024

3. UGC: Universal Graph Coarsening,  with Mohit Kataria, NeurIPS 2024

4. 𝗖𝗼𝗥𝗘-𝗕𝗢𝗟𝗗, a unified framework for Cross-Domain Robust and Equitable Ensemble for BOLD Signal Analysis,  with Vipul, Jyotismita, at ml4H 2024

5. No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation, with Nimesh, Anuj, and Jayadeva, AAAI 2024. 

6. Graph of Circuits with GNN for Exploring the Optimal Design Space" with: Aditya, Sapna, and Ankesh Jain,  Neural Information Processing Systems (NeurIPS), 2023.

7. Featured Graph Coarsening with Similarity Guarantee, with Manoj Kumar, Anurag Sharma, Shaswat Saxena, International Conference in Machine  Learning, Hawai, US, ICML 2023.

8. Structured Graph Learning via Laplacian Spectral Constraints, With J. Ying, Ze. Vinicius, and D. Palomar, Neural Information Processing Systems (NeurIPS), Canada, December 2019 [Paper],[Slides], [2-min video], [Poster], [R Package].

Other Conferences (< 8 pages)

1. ``ArtTwin: A Novel Concept of Developing Digital Twin of Human Arterial System, with Hemanthika and Sitikantha,  IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2024.

2. PPDA: A Privacy Preserving Framework for Distributed Graph Learning, with Nikita and Nimesh ICONIP 2024. 

3. Estimating Normalized Graph Laplacians in Financial Markets, with Ze Vinicius, Jiaxi Ying, and Daniel Palomar, submitted to ICASSP 2023.

4. ROBUST AND GLOBALLY SPARSE PCA VIA MAJORIZATION-MINIMIZATION AND VARIABLE SPLITTING, with Arnaud Breloy, submitted to ICASSP 2023

5. Robustifying GNN Via Weighted Laplacian, with Bharat Runwal and Vivek, IEEE SPCOMM Bangalore 2022. Got the best paper award[Paper].

6. Parameter estimation for Students t VAR Model with Missing Data", With R. Zhou, J. Liu, and D. Palomar  ICASSP 2021.

7. Bipartite Structured Gaussian Graphical Modelling via Adjacency Spectral Priors, With: J. Ying, Ze. Vinicius, and D. Palomar, IEEE ACSSC, Asilomar, U.S, 2019.

8. Parameter Estimation of Heavy-Tailed Random AR(p) Model from Incomplete Data, With: J.Liu and D. Palomar, IEEE EUSIPCO, Coruna, Spain, 2019.

9. Parameter Estimation of Heavy-Tailed Random Walk Model from Incomplete Data, With: J.Liu and D. Palomar, IEEE ICASSP, Calgary, Canada, April 2018.

10. Asynchronous Localization over WSN via Non-Convex Consensus ADMM, With K. Rajawat, NCC, Chennai, India, 2017.

11. Distributed Interference Alignment for MIMO Cellular Network via Consensus ADMM, With K. Rajawat, IEEE GlobablSIP, Washington, US, 2016.

12. Velocity Assisted Multidimensional Scaling, With K. Rajawat, IEEE SPAWC, Stockholm, Sweden, 2015.