Nanda Gopal Parise
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Research Areas
Biography and Research Information
OverviewAI-generated summary
Nanda Gopal Parise's research focuses on advanced computational techniques, particularly in the areas of artificial intelligence and cybersecurity. His work investigates the application of machine learning models for complex tasks such as extreme weather forecasting and malware classification, exploring the effectiveness of hierarchical evidence-aware transformers and dual-path attentive CNNs. Parise also studies the vulnerabilities within deep neural networks, examining issues like silent weight corruption, adversarial fragility, and the development of lightweight integrity defenses against GPU memory fault attacks. Additionally, his research extends to detecting stealthy side-channel attacks in multi-tenant cloud environments. He has a recent publication in 2026 and has collaborated with Prasanna Kumar Reddy Mallampati 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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