Sandeep Kumar - RankMe

45 papers A* 5B 1Misc 5Journal 22Unranked 12

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	Bonn EEG as Graphs: Structural vs. Recurrence Networks for Tiny-Data Seizure Screening. NCC Ekta Srivastava, Siddhant Ujjain, Tapan Kumar Gandhi, Sandeep Kumar
2026	A*	`conf`	Leap of FAITH from GNN-to-MLP: Fairness Aware Inference via DisTillation of GrapH Knowledge. AAAI Vipul Kumar Singh, Jyotismita Barman, Sandeep Kumar, Tapan K. Gandhi, Jayadeva
2025	J	`jnl`	A Unified Optimization-Based Framework for Certifiably Robust and Fair Graph Neural Networks. IEEE Trans. Signal Process. Vipul Kumar Singh, Sandeep Kumar, Avadhesh Prasad, Jayadeva
2025	J	`jnl`	A novel coarsened graph learning method for scalable single-cell data analysis. Comput. Biol. Medicine Mohit Kataria, Ekta Srivastava, Kumar Arjun, Sandeep Kumar, Ishaan Gupta, Jayadeva
2025	J	`jnl`	AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening. CoRR Mohit Kataria, Shreyash Bhilwade, Sandeep Kumar, Jayadeva
2025	J	`jnl`	An Efficient Framework for Epidemiological Parameter Estimation via Graph Reduction and Graph Neural Networks. ACM Trans. Knowl. Discov. Data Muhammad S. T. Alfas, Manoj Kumar, Shaurya Shriyam, Sandeep Kumar
2025	—	`conf`	Coarse-and-Learn: Efficient Online Node Labeling. ICONIP (4) Subhanu Halder, Manoj Kumar, Yifan Sun, Sandeep Kumar
2025	J	`jnl`	CrossMed: A Multimodal Cross-Task Benchmark for Compositional Generalization in Medical Imaging. CoRR Pooja Singh, Siddhant Ujjain, Tapan Kumar Gandhi, Sandeep Kumar
2025	J	`jnl`	GOTHAM: Graph Class Incremental Learning Framework under Weak Supervision. CoRR Aditya Hemant Shahane, Prathosh A. P., Sandeep Kumar
2025	J	`jnl`	GraphFLEx: Structure Learning Framework for Large Expanding Graphs. CoRR Mohit Kataria, Nikita Malik, Sandeep Kumar, Jayadeva
2025	—	`conf`	Multi-Component Coarsened Graph Learning for Scaling Graph Machine Learning. WWW (Companion Volume) Subhanu Halder, Manoj Kumar, Sandeep Kumar
2025	B	`conf`	REFINE: Enabling Efficient and Trustworthy Modeling of Financial Networks via GNN-to-MLP Knowledge Distillation. DSAA Vipul Kumar Singh, Jyotismita Barman, Sandeep Kumar, Jayadeva
2024	—	`conf`	CoRE-BOLD: Cross-Domain Robust and Equitable Ensemble for BOLD Signal Analysis. ML4H@NeurIPS Vipul Kumar Singh, Jyotismita Barman, Sandeep Kumar, Jayadeva
2024	—	`conf`	HistoGraphCoarse: Strategizing Graph Coarsening Techniques for Efficient Analysis of Gigapixel Histopathological Images. GRAIL@MICCAI Ekta Srivastava, Syed Mohammed Danish, Kumar Arjun, Manoj Kumar, Mohit Kataria, Syed Farhan Abbas, Ishaan Gupta, Sandeep Kumar
2024	A*	`conf`	No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation. AAAI Nimesh Agrawal, Anuj Kumar Sirohi, Sandeep Kumar, Jayadeva
2024	A*	`conf`	UGC: Universal Graph Coarsening. NeurIPS Mohit Kataria, Sandeep Kumar, Jayadeva
2024	—	`conf`	Unveiling Graph Structures for Machine Learning: Learning, Structuring, and Coarsening. COMAD/CODS Manoj Kumar, Sandeep Kumar
2023	J	`jnl`	Alzheimer's Disease Is Associated with Increased Network Assortativity: Evidence from Metabolic Connectivity. Brain Connect. Sunil Kumar Khokhar, Manoj Kumar, Sandeep Kumar, Tejaswini Manae, Nithin Thanissery, Subasree Ramakrishnan, Faheem Arshad, Chandana Nagaraj, Sandhya Mangalore, Suvarna Alladi, Tapan K. Gandhi, Rose Dawn Bharath
2023	—	`conf`	Does Spatial Location of The Electrodes in EEG Matter for Tracking the Brain States? NCC Raghav Dev, Sandeep Kumar, Tapan Kumar Gandhi
2023	Misc	`conf`	Estimating Normalized Graph Laplacians in Financial Markets. ICASSP José Vinícius de Miranda Cardoso, Jiaxi Ying, Sandeep Kumar, Daniel P. Palomar
2023	A*	`conf`	Graph of Circuits with GNN for Exploring the Optimal Design Space. NeurIPS Aditya Hemant Shahane, Saripilli Swapna Manjiri, Ankesh Jain, Sandeep Kumar
2023	J	`jnl`	No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation. CoRR Nimesh Agrawal, Anuj Kumar Sirohi, Jayadeva, Sandeep Kumar
2023	Misc	`conf`	Robust and Globally Sparse Pca via Majorization-Minimization and Variable Splitting. ICASSP Hugo Brehier, Arnaud Breloy, Mohammed Nabil El Korso, Sandeep Kumar
2021	J	`jnl`	Majorization-Minimization on the Stiefel Manifold With Application to Robust Sparse PCA. IEEE Trans. Signal Process. Arnaud Breloy, Sandeep Kumar, Ying Sun, Daniel P. Palomar
2021	Misc	`conf`	Parameter Estimation for Student's t VAR Model with Missing Data. ICASSP Rui Zhou, Junyan Liu, Sandeep Kumar, Daniel P. Palomar
2020	J	`jnl`	A Unified Framework for Structured Graph Learning via Spectral Constraints. J. Mach. Learn. Res. Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
2020	J	`jnl`	Student's $t$ VAR Modeling With Missing Data Via Stochastic EM and Gibbs Sampling. IEEE Trans. Signal Process. Rui Zhou, Junyan Liu, Sandeep Kumar, Daniel P. Palomar
2019	J	`jnl`	A Unified Framework for Structured Graph Learning via Spectral Constraints. CoRR Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
2019	Misc	`conf`	Bipartite Structured Gaussian Graphical Modeling via Adjacency Spectral Priors. ACSSC Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
2019	J	`jnl`	Distributed Inexact Successive Convex Approximation ADMM: Analysis-Part I. CoRR Sandeep Kumar, Ketan Rajawat, Daniel P. Palomar
2019	J	`jnl`	Optimization Algorithms for Graph Laplacian Estimation via ADMM and MM. IEEE Trans. Signal Process. Licheng Zhao, Yiwei Wang, Sandeep Kumar, Daniel P. Palomar
2019	J	`jnl`	Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. Signal Process. Junyan Liu, Sandeep Kumar, Daniel P. Palomar
2019	—	`conf`	Parameter Estimation of Heavy-Tailed AR(p) Model from Incomplete Data. EUSIPCO Junyan Liu, Sandeep Kumar, Daniel P. Palomar
2019	—	`conf`	Robust Factor Analysis Parameter Estimation. EUROCAST (2) Rui Zhou, Junyan Liu, Sandeep Kumar, Daniel P. Palomar
2019	A*	`conf`	Structured Graph Learning Via Laplacian Spectral Constraints. NeurIPS Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
2019	J	`jnl`	Structured Graph Learning Via Laplacian Spectral Constraints. CoRR Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
2018	Misc	`conf`	Parameter Estimation of Heavy-Tailed Random Walk Model from Incomplete Data. ICASSP Junyan Liu, Sandeep Kumar, Daniel P. Palomar
2017	J	`jnl`	Asynchronous Optimization Over Heterogeneous Networks Via Consensus ADMM. IEEE Trans. Signal Inf. Process. over Networks Sandeep Kumar, Rahul Jain, Ketan Rajawat
2017	—	`conf`	Distributed asynchronous localization over WSNs via non-convex consensus ADMM. NCC Sandeep Kumar, Ketan Rajawat
2017	J	`jnl`	Stochastic Multidimensional Scaling. IEEE Trans. Signal Inf. Process. over Networks Ketan Rajawat, Sandeep Kumar
2016	J	`jnl`	Asynchronous Optimization Over Heterogeneous Networks via Consensus ADMM. CoRR Sandeep Kumar, Rahul Jain, Ketan Rajawat
2016	J	`jnl`	Cooperative Localization of Mobile Networks Via Velocity-Assisted Multidimensional Scaling. IEEE Trans. Signal Process. Sandeep Kumar, Raju Kumar, Ketan Rajawat
2016	—	`conf`	Distributed interference alignment for MIMO cellular network via consensus ADMM. GlobalSIP Sandeep Kumar, Ketan Rajawat
2016	J	`jnl`	Stochastic Multidimensional Scaling. CoRR Ketan Rajawat, Sandeep Kumar
2015	—	`conf`	Velocity-assisted multidimensional scaling. SPAWC Sandeep Kumar, Ketan Rajawat

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.