Updated October 2026. Published work, accepted papers, and preprints are labeled below. Illustrated archive and BibTeX citations ↗
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 · Preprint
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.
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 · Preprint
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.
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 · Accepted
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.
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 · Accepted
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.
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 · Preprint
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.
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.
Paper ↗ · arXiv ↗ · PDF ↗ · Code ↗
The 2025 arXiv version has an earlier title and six authors; the citation here follows the published KDD 2026 paper.
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.
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.
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.
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.
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.
Paper ↗ · arXiv ↗ · PDF ↗ · Code ↗
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.
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 · Best Paper Award · HST 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.
Paper ↗ · PNNL summary ↗ · Related code ↗
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.
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 · Best Application Paper · DSAA 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.
Paper ↗ · arXiv ↗ · PDF ↗ · Code ↗
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.
Paper ↗ · Code ↗ · Author manuscript ↗
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.
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.