Nanda Gopal Parise
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Researcher
Also affiliated: Southern Arkansas University Tech (2026)
Unknown Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Nanda Gopal Parise's research focuses on the application of artificial intelligence and machine learning techniques across various domains. His recent publications include work on hierarchical evidence-aware transformers for malware classification using volatile memory artifacts, and evaluating GPU memory fault attacks on deep neural networks with lightweight integrity defenses. He has also investigated cross-layer detection of stealthy side-channel attacks in multi-tenant clouds and analyzed the adversarial fragility in deep neural networks. Other research areas include federated graph neural networks for decentralized movie recommendations and dual-path attentive convolutional neural networks for facial pain expression classification. Parise also explores the role of AI in climate change prediction, specifically analyzing machine learning models for extreme weather forecasting. He has collaborated with Prasanna Kumar Reddy Mallampati at Southern Arkansas University on four shared publications.
Metrics
- h-index: 1
- Publications: 7
- Citations: 1
Selected Publications
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MemFormer-H: MemFormer-H: A Hierarchical Evidence-Aware Transformer for Malware Classification Using Volatile Memory Artifacts (2026)
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Silent Weight Corruption: Evaluating GPU Memory Fault Attacks on Deep Neural Networks and Designing Lightweight Integrity Defenses (2026)
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Cross-Layer Detection of Stealthy Side-Channel Attacks in Multi-Tenant Clouds with Statistical False-Positive Control (2026)
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Adversarial Fragility in Deep Neural Networks: Structural Causes, Theoretical Limits, and the Illusion of Robustness (2026)
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The Role of AI in Climate Change Prediction: Analysing Machine Learning Models for Extreme Weather Forecasting (2026)
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FedGraphDiff: A Federated Graph Neural Network Framework for Decentralized Movie Recommendations (2025)
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Dual-Path Attentive CNN for Facial Pain Expression Classification Across Diverse Identities (2025)
Collaboration Network
Top Collaborators
- Adversarial Fragility in Deep Neural Networks: Structural Causes, Theoretical Limits, and the Illusion of Robustness
- Cross-Layer Detection of Stealthy Side-Channel Attacks in Multi-Tenant Clouds with Statistical False-Positive Control
- Silent Weight Corruption: Evaluating GPU Memory Fault Attacks on Deep Neural Networks and Designing Lightweight Integrity Defenses
- MemFormer-H: MemFormer-H: A Hierarchical Evidence-Aware Transformer for Malware Classification Using Volatile Memory Artifacts
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