Authors: Jugal Gajjar, Kaustik Ranaware, Kamalasankari Subramaniakuppusamy
Our hybrid framework combines heterogeneous graph representations with local LLMs for Java vulnerability detection. Achieves 93.57% accuracy — an 8.36% gain over GAT embeddings and 17.81% over pretrained LLM baselines. Extracts salient subgraphs with natural language explanations.
Authors: Jugal Gajjar, Kamalasankari Subramaniakuppusamy, Relsy Puthal, Kaustik Ranaware
Hybrid repair framework integrating Bandit with lightweight local LLMs (<8B params) in an iterative detect-repair-validate loop. Reduces false positives by 10.8%, improves fix accuracy by 13.51%. Developer explanation quality: 4.5/5.
Authors: Jugal Gajjar, Kamalasankari Subramaniakuppusamy, Noha El Kachach
Language-agnostic multi-stage AI pipeline using fine-tuned Qwen2.5-Coder-3B with LoRA within MLX framework. Covers 14 programming languages. Usefulness: 8.06/10, interpretability: 7.40/10, readability: 7.53/10.
Authors: Jugal Gajjar, Kamalasankari Subramaniakuppusamy
Large-scale language-agnostic dataset unifying code across ten major programming languages. Over seven million parsed source files under a universal AST schema for cross-language reasoning and multilingual software analysis. Published on Hugging Face.
Authors: Jugal Gajjar, Kaustik Ranaware
Multimodal sentiment analysis on CMU-MOSEI using transformer-based models with early fusion of text, audio, and visual modalities. Achieves 97.87% 7-class accuracy and 0.9682 F1-score.
Authors: Jugal Gajjar, Sanjana Nathani
Proposes the Sentient AI Framework (SAIF) for integrating emotional intelligence in AI systems. Discusses cultivating trust and empathy in human-AI interaction, paving the way for AI integration in society and policy-making.