Browse papers and preprints below, with summaries, figures, and research resources. Preprints and accepted papers are labeled by status.

2026

Figure 2 · EDiS pipeline from EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks
Figure 2 · EDiS pipeline
arXiv:2610.09059

EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks

Sai Karthik Navuluru, Siddhartha Shankar Das, Franck Dernoncourt, S M Ferdous, Ryan A. Rossi, Nesreen K. Ahmed, Baris Coskunuzer, Alex Pothen, Lakshman Tamil, and Mahantesh M Halappanavar

arXiv:2610.09059 · 2026

Decompose a graph once, then reuse cached edge-disjoint subgraphs to compose a different sparse training graph each epoch. EDiS separates structural extraction from epoch-level sampling and evaluates this approach across 19 node classification benchmarks.

BibTeX citation
@misc{das_edis_2026,
  title = {EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks},
  author = {Sai Karthik Navuluru and Siddhartha Shankar Das and Franck Dernoncourt and S M Ferdous and Ryan A. Rossi and Nesreen K. Ahmed and Baris Coskunuzer and Alex Pothen and Lakshman Tamil and Mahantesh M Halappanavar},
  year = {2026},
  eprint = {2610.09059},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.09059}
}
Figure 2 · Support-graph structure from Scaffold: Support Graph Theory Based Sparsification for Graph Neural Networks
Figure 2 · Support-graph structure
arXiv:2609.31466

Scaffold: Support Graph Theory Based Sparsification for Graph Neural Networks

Siddhartha Shankar Das, Sai Karthik Navuluru, S M Ferdous, Ryan A. Rossi, Baris Coskunuzer, Lakshman Tamil, Edoardo Serra, Alex Pothen, Robert Rallo, and Mahantesh M Halappanavar

arXiv:2609.31466 · 2026

An unsupervised sparsification framework that controls the length of rerouted communication paths and the load they place on retained edges. Across 19 benchmarks, sparse supports retain 10–50% of edges while recovering or closely approaching full-graph GNN performance.

BibTeX citation
@misc{das_scaffold_2026,
  title = {Scaffold: Support Graph Theory Based Sparsification for Graph Neural Networks},
  author = {Siddhartha Shankar Das and Sai Karthik Navuluru and S M Ferdous and Ryan A. Rossi and Baris Coskunuzer and Lakshman Tamil and Edoardo Serra and Alex Pothen and Robert Rallo and Mahantesh M Halappanavar},
  year = {2026},
  eprint = {2609.31466},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.31466}
}
HPEC 2026Accepted

Diff-GNN: Differentiable Graph Neural Networks for Joint Hardware–Software Partitioning and Scheduling

Siddhartha Shankar Das, Zakaria Mehrab, James Kotary, Rounak Meyur, S. M. Ferdous, Erdal Mutlu, and Mahantesh Halappanavar

IEEE High Performance Extreme Computing Conference (HPEC) · 2026

Differentiable graph learning for jointly assigning computation to hardware or software and scheduling its execution. This work connects learned graph representations with system design and optimization under resource and communication constraints.

Accepted at HPEC 2026; listed in the September 17 conference program. Public paper and code links will be added when available.

BibTeX citation
@inproceedings{das_diff_gnn_2026,
  title = {Diff-GNN: Differentiable Graph Neural Networks for Joint Hardware–Software Partitioning and Scheduling},
  author = {Siddhartha Shankar Das and Zakaria Mehrab and James Kotary and Rounak Meyur and S. M. Ferdous and Erdal Mutlu and Mahantesh Halappanavar},
  year = {2026},
  booktitle = {IEEE High Performance Extreme Computing Conference (HPEC)},
  url = {https://ieee-hpec.org/ieee-hpec-2026-agenda/}
}
HPEC 2026Accepted

Joint Optimization of HW/SW Partitioning and Scheduling with Deep Reinforcement Learning

James Kotary, Zakaria Mehrab, Siddhartha Shankar Das, Rounak Meyur, S. M. Ferdous, Erdal Mutlu, and Mahantesh Halappanavar

IEEE High Performance Extreme Computing Conference (HPEC) · 2026

Reinforcement learning for the coupled decisions of hardware/software partitioning and task scheduling. The conference program lists this work as “Joint Optimization of HW/SW Partitioning and Scheduling with Policy Gradient Methods.”

Title follows the October 2026 CV; the official program uses the policy-gradient title. Accepted status follows the CV.

BibTeX citation
@inproceedings{das_hw_sw_rl_2026,
  title = {Joint Optimization of HW/SW Partitioning and Scheduling with Deep Reinforcement Learning},
  author = {James Kotary and Zakaria Mehrab and Siddhartha Shankar Das and Rounak Meyur and S. M. Ferdous and Erdal Mutlu and Mahantesh Halappanavar},
  year = {2026},
  booktitle = {IEEE High Performance Extreme Computing Conference (HPEC)},
  url = {https://ieee-hpec.org/ieee-hpec-2026-agenda/}
}
Figure 1 · Unified GNN design space from Unifying Graph Neural Networks Through a Common Layer Equation
Figure 1 · Unified GNN design space
arXiv:2608.16097

Unifying Graph Neural Networks Through a Common Layer Equation

Sai Karthik Navuluru, Siddhartha Shankar Das, Bo Ni, Hongjie Chen, Yu Wang, Baris Coskunuzer, Nesreen K. Ahmed, Franck Dernoncourt, Mahantesh Halappanavar, Tyler Derr, Ryan A. Rossi, and Lakshman Tamil

arXiv:2608.16097 · 2026

A common layer equation organizes more than 200 GNN architectures into seven components. Separating propagation from message transformation makes architectural similarities explicit and supports component-level comparison, theoretical analysis, and architecture generation.

BibTeX citation
@misc{das_unifying_2026,
  title = {Unifying Graph Neural Networks Through a Common Layer Equation},
  author = {Sai Karthik Navuluru and Siddhartha Shankar Das and Bo Ni and Hongjie Chen and Yu Wang and Baris Coskunuzer and Nesreen K. Ahmed and Franck Dernoncourt and Mahantesh Halappanavar and Tyler Derr and Ryan A. Rossi and Lakshman Tamil},
  year = {2026},
  eprint = {2608.16097},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.16097}
}
Figure 1 · SGS-GNN architecture (preprint) from SGS-GNN: A Supervised Graph Sparsifier for Graph Neural Networks
Figure 1 · SGS-GNN architecture (preprint)
KDD 2026

SGS-GNN: A Supervised Graph Sparsifier for Graph Neural Networks

Siddhartha Shankar Das, Naheed Anjum Arafat, Muftiqur Rahman, S M Ferdous, Alex Pothen, Mahantesh Halappanavar, and Danda B. Rawat

Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, pp. 755–766 · 2026

Learn which graph edges matter for a downstream prediction task. SGS-GNN trains an edge probability model and a GNN together to sample compact, task-relevant subgraphs, reducing memory requirements while preserving predictive signal.

The 2025 arXiv version has an earlier title and six authors; the citation here follows the published KDD 2026 paper.

BibTeX citation
@inproceedings{das_sgs_gnn_2026,
  title = {SGS-GNN: A Supervised Graph Sparsifier for Graph Neural Networks},
  author = {Siddhartha Shankar Das and Naheed Anjum Arafat and Muftiqur Rahman and S M Ferdous and Alex Pothen and Mahantesh Halappanavar and Danda B. Rawat},
  year = {2026},
  booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, pp. 755–766},
  doi = {10.1145/3770855.3818110},
  eprint = {2502.10208},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1145/3770855.3818110}
}

2025

Figure 1 · Knowledge graph pipeline from ATT&CK to CVE: A Large-Scale Automated Knowledge Graph for Threat Intelligence
Figure 1 · Knowledge graph pipeline
ICDMW / GTA³ 2025

ATT&CK to CVE: A Large-Scale Automated Knowledge Graph for Threat Intelligence

Moqsadur Rahman, Monika Akbar, Joseph T. Aguayo, Mahmud Shahriar Hossain, Tina M. Ellis, Siddhartha Shankar Das, Mohammad Fakhruddin Babar, Monowar Hasan, Aaron Sanchez, Luis Fernando de la Torre, and Mahantesh Halappanavar

IEEE International Conference on Data Mining Workshops (ICDMW), GTA³ Workshop · 2025

A continuously updated cybersecurity knowledge graph integrates CVE, CWE, CAPEC, and ATT&CK data in Neo4j. Automated ingestion and typed relationships support multi-hop threat analysis and connections between adversary behavior and software vulnerabilities.

BibTeX citation
@inproceedings{das_attack2cve_2025,
  title = {ATT\&CK to CVE: A Large-Scale Automated Knowledge Graph for Threat Intelligence},
  author = {Moqsadur Rahman and Monika Akbar and Joseph T. Aguayo and Mahmud Shahriar Hossain and Tina M. Ellis and Siddhartha Shankar Das and Mohammad Fakhruddin Babar and Monowar Hasan and Aaron Sanchez and Luis Fernando de la Torre and Mahantesh Halappanavar},
  year = {2025},
  booktitle = {IEEE International Conference on Data Mining Workshops (ICDMW), GTA³ Workshop},
  doi = {10.1109/ICDMW69685.2025.00140},
  url = {https://doi.org/10.1109/ICDMW69685.2025.00140}
}
PNNL 2025

NEPATEC v2.0: Standardized Metadata and Text Corpus of National Environmental Policy Act Documents

Sai Munikoti, Dan Nally, Sai Dileep Koneru, Siddhartha Shankar Das, Kaustav Bhattacharjee, Ashik Islam, Alex Buchko, Taylor Edwards, Kathy Nwe, Siddhisanket Raskar, Paul Rigor, Heng Wan, Micah Taylor, Scott Spare, Derek Lilienthal, Mahantesh Halappanavar, Anurag Acharya, Tim Vega, Mike Parker, Anastasia Bernat, and Sameera Horawalavithana

Pacific Northwest National Laboratory, PNNL-38163 · 2025

A public corpus connects NEPA documents with standardized project, process, and document metadata. The report describes more than 120,000 documents from roughly 60,000 projects and over 60 agencies, enabling research in document intelligence and environmental permitting.

BibTeX citation
@techreport{das_nepatec_2025,
  title = {NEPATEC v2.0: Standardized Metadata and Text Corpus of National Environmental Policy Act Documents},
  author = {Sai Munikoti and Dan Nally and Sai Dileep Koneru and Siddhartha Shankar Das and Kaustav Bhattacharjee and Ashik Islam and Alex Buchko and Taylor Edwards and Kathy Nwe and Siddhisanket Raskar and Paul Rigor and Heng Wan and Micah Taylor and Scott Spare and Derek Lilienthal and Mahantesh Halappanavar and Anurag Acharya and Tim Vega and Mike Parker and Anastasia Bernat and Sameera Horawalavithana},
  year = {2025},
  institution = {Pacific Northwest National Laboratory, PNNL-38163},
  url = {https://www.pnnl.gov/publications/nepatec-v20-standardized-metadata-and-text-corpus-national-environmental-policy-act}
}
Wiley 2025

Generative AI for Advanced Cyber Defense

Moqsadur Rahman, Aaron Sanchez, Krish Piryani, Siddhartha Shankar Das, Sai Munikoti, Luis de la Torre Quintana, Monowar Hasan, Joseph Aguayo, Monika Akbar, Shahriar Hossain, and Mahantesh Halappanavar

AI for Cybersecurity: Research and Practice, John Wiley & Sons, pp. 109–146 · 2025

A book chapter on generative AI for cyber defense, connecting language models, security knowledge, and retrieval-based methods with practical cybersecurity applications.

BibTeX citation
@incollection{das_genai_cyber_2025,
  title = {Generative AI for Advanced Cyber Defense},
  author = {Moqsadur Rahman and Aaron Sanchez and Krish Piryani and Siddhartha Shankar Das and Sai Munikoti and Luis de la Torre Quintana and Monowar Hasan and Joseph Aguayo and Monika Akbar and Shahriar Hossain and Mahantesh Halappanavar},
  year = {2025},
  booktitle = {AI for Cybersecurity: Research and Practice, John Wiley \& Sons, pp. 109–146},
  doi = {10.1002/9781394293773.ch04},
  url = {https://doi.org/10.1002/9781394293773.ch04}
}
Purdue 2025

Scaling Graph Neural Networks for Large Graphs and Analyzing Common Vulnerabilities via Large Language Models

Siddhartha Shankar Das

Ph.D. dissertation, Purdue University. Advisor: Alex Pothen · 2025

Doctoral research on scalable GNN sampling and sparsification, together with language-model methods for vulnerability classification and analysis.

BibTeX citation
@phdthesis{das_dissertation_2025,
  title = {Scaling Graph Neural Networks for Large Graphs and Analyzing Common Vulnerabilities via Large Language Models},
  author = {Siddhartha Shankar Das},
  year = {2025},
  school = {Purdue University},
  url = {https://hammer.purdue.edu/articles/thesis/Scaling_Graph_Neural_Networks_for_Large_Graphs_and_Analyzing_Common_Vulnerabilities_via_Large_Language_Models/28856864}
}

2024

Figure 3 · Attribute-guided sampling from AGS-GNN: Attribute-guided Sampling for Graph Neural Networks
Figure 3 · Attribute-guided sampling
KDD 2024

AGS-GNN: Attribute-guided Sampling for Graph Neural Networks

Siddhartha Shankar Das, S M Ferdous, Mahantesh M. Halappanavar, Edoardo Serra, and Alex Pothen

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 538–549 · 2024

Attribute-guided sampling combines feature similarity and feature diversity to learn from both homophilic and heterophilic neighborhoods. Dual channels adapt to local graph structure, with evaluations on 35 small and large graphs.

BibTeX citation
@inproceedings{das_ags_gnn_2024,
  title = {AGS-GNN: Attribute-guided Sampling for Graph Neural Networks},
  author = {Siddhartha Shankar Das and S M Ferdous and Mahantesh M. Halappanavar and Edoardo Serra and Alex Pothen},
  year = {2024},
  booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 538–549},
  doi = {10.1145/3637528.3671940},
  eprint = {2405.15218},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1145/3637528.3671940}
}

2022

IEEE Big Data 2022

VWC-BERT: Scaling Vulnerability–Weakness–Exploit Mapping on Modern AI Accelerators

Siddhartha Shankar Das, Mahantesh Halappanavar, Antonino Tumeo, Edoardo Serra, Alex Pothen, and Ehab Al-Shaer

IEEE International Conference on Big Data, pp. 1224–1229 · 2022

Scalable language-model methods connect software vulnerabilities to weaknesses and exploits. VWC-BERT studies training and inference on modern AI accelerators to make vulnerability analysis practical at larger scales.

BibTeX citation
@inproceedings{das_vwc_bert_2022,
  title = {VWC-BERT: Scaling Vulnerability–Weakness–Exploit Mapping on Modern AI Accelerators},
  author = {Siddhartha Shankar Das and Mahantesh Halappanavar and Antonino Tumeo and Edoardo Serra and Alex Pothen and Ehab Al-Shaer},
  year = {2022},
  booktitle = {IEEE International Conference on Big Data, pp. 1224–1229},
  doi = {10.1109/BigData55660.2022.10020622},
  url = {https://doi.org/10.1109/BigData55660.2022.10020622}
}
HST 2022Best Paper Award · HST 2022

Towards Automatic Mapping of Vulnerabilities to Attack Patterns Using Large Language Models

Siddhartha Shankar Das, Ashutosh Dutta, Sumit Purohit, Edoardo Serra, Mahantesh Halappanavar, and Alex Pothen

IEEE International Symposium on Technologies for Homeland Security (HST), pp. 1–7 · 2022

VWC-MAP links vulnerability descriptions to attack patterns through a two-tier pipeline: vulnerability-to-weakness classification followed by weakness-to-attack mapping using text-to-text and link prediction methods.

BibTeX citation
@inproceedings{das_vwc_map_2022,
  title = {Towards Automatic Mapping of Vulnerabilities to Attack Patterns Using Large Language Models},
  author = {Siddhartha Shankar Das and Ashutosh Dutta and Sumit Purohit and Edoardo Serra and Mahantesh Halappanavar and Alex Pothen},
  year = {2022},
  booktitle = {IEEE International Symposium on Technologies for Homeland Security (HST), pp. 1–7},
  doi = {10.1109/HST56032.2022.10025459},
  url = {https://doi.org/10.1109/HST56032.2022.10025459}
}
HST 2022

Efficient Clustering of Software Vulnerabilities using Self Organizing Map (SOM)

K. Panchal, Siddhartha Shankar Das, L. De La Torre, J. Miller, R. Rallo, and M. Halappanavar

IEEE International Symposium on Technologies for Homeland Security (HST), pp. 1–7 · 2022

Self-organizing maps group software vulnerabilities to support the exploration and analysis of large vulnerability collections.

BibTeX citation
@inproceedings{das_som_2022,
  title = {Efficient Clustering of Software Vulnerabilities using Self Organizing Map (SOM)},
  author = {K. Panchal and Siddhartha Shankar Das and L. De La Torre and J. Miller and R. Rallo and M. Halappanavar},
  year = {2022},
  booktitle = {IEEE International Symposium on Technologies for Homeland Security (HST), pp. 1–7},
  doi = {10.1109/HST56032.2022.10025443},
  url = {https://doi.org/10.1109/HST56032.2022.10025443}
}

2021

Figure 2 · V2W-BERT architecture from V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities
Figure 2 · V2W-BERT architecture
DSAA 2021Best Application Paper · DSAA 2021

V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities

Siddhartha Shankar Das, Edoardo Serra, Mahantesh Halappanavar, Alex Pothen, and Ehab Al-Shaer

IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–12 · 2021

A BERT-based framework maps vulnerability descriptions to a hierarchy of software weaknesses. Link prediction and a reconstruction objective support classification even when labeled examples for particular weaknesses are scarce.

BibTeX citation
@inproceedings{das_v2w_bert_2021,
  title = {V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities},
  author = {Siddhartha Shankar Das and Edoardo Serra and Mahantesh Halappanavar and Alex Pothen and Ehab Al-Shaer},
  year = {2021},
  booktitle = {IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–12},
  doi = {10.1109/DSAA53316.2021.9564227},
  eprint = {2102.11498},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1109/DSAA53316.2021.9564227}
}

2019

Figure 2 · Preferred reference points (author manuscript) from Evolutionary algorithm using adaptive fuzzy dominance and reference point for many-objective optimization
Figure 2 · Preferred reference points (author manuscript)
Swarm and Evolutionary Computation 2019

Evolutionary algorithm using adaptive fuzzy dominance and reference point for many-objective optimization

Siddhartha Shankar Das, Md Monirul Islam, and Naheed Anjum Arafat

Swarm and Evolutionary Computation, 44, pp. 1092–1107 · 2019

An evolutionary optimization method combines adaptive fuzzy dominance and reference points to balance convergence and diversity when optimizing many competing objectives.

BibTeX citation
@article{das_fdea_2019,
  title = {Evolutionary algorithm using adaptive fuzzy dominance and reference point for many-objective optimization},
  author = {Siddhartha Shankar Das and Md Monirul Islam and Naheed Anjum Arafat},
  year = {2019},
  journal = {Swarm and Evolutionary Computation, 44, pp. 1092–1107},
  doi = {10.1016/j.swevo.2018.11.003},
  url = {https://doi.org/10.1016/j.swevo.2018.11.003}
}

2018

NSysS 2018

Exploiting a Synergy between Greedy Approach and NSGA for Scheduling in Computing Clusters

A. S. M. Rizvi, Tarik Reza Toha, Siddhartha Shankar Das, Sriram Chellappan, and A. B. M. Alim Al Islam

5th International Conference on Networking, Systems and Security (NSysS), pp. 1–8 · 2018

Cluster scheduling research combining greedy decisions with nondominated sorting genetic algorithms to handle competing performance objectives.

BibTeX citation
@inproceedings{das_greedy_nsga_2018,
  title = {Exploiting a Synergy between Greedy Approach and NSGA for Scheduling in Computing Clusters},
  author = {A. S. M. Rizvi and Tarik Reza Toha and Siddhartha Shankar Das and Sriram Chellappan and A. B. M. Alim Al Islam},
  year = {2018},
  booktitle = {5th International Conference on Networking, Systems and Security (NSysS), pp. 1–8},
  doi = {10.1109/NSysS.2018.8631376},
  url = {https://doi.org/10.1109/NSysS.2018.8631376}
}

2017

IPCCC 2017

Many-objective performance enhancement in computing clusters

A. S. M. Rizvi, Tarik Reza Toha, Siddhartha Shankar Das, Sriram Chellappan, and A. B. M. Alim Al Islam

IEEE International Performance Computing and Communications Conference (IPCCC), pp. 1–2 · 2017

A many-objective view of cluster performance optimization, studying scheduling decisions across multiple competing system goals.

BibTeX citation
@inproceedings{das_cluster_2017,
  title = {Many-objective performance enhancement in computing clusters},
  author = {A. S. M. Rizvi and Tarik Reza Toha and Siddhartha Shankar Das and Sriram Chellappan and A. B. M. Alim Al Islam},
  year = {2017},
  booktitle = {IEEE International Performance Computing and Communications Conference (IPCCC), pp. 1–2},
  doi = {10.1109/PCCC.2017.8280491},
  url = {https://doi.org/10.1109/PCCC.2017.8280491}
}

Updated October 15, 2026. Figure excerpts are from the linked papers. Summaries describe the individual studies; paper links provide the full methods and results.