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Jugal Gajjar

Jugal Gajjar

Graduate student at The George Washington University working on reasoning-centric AI systems with a focus on Large Language Models, trustworthy and explainable AI, and AI for code intelligence and security.

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About Me
I design and study reasoning-centric AI systems that can understand, reason, and justify their decisions. My research focuses on improving the faithfulness, interpretability, and reliability of Large Language Models (LLMs) through structured reasoning, attribution, and reinforcement learning.

I also explore applications in code intelligence and software security, where I combine graph learning and LLMs to build systems for vulnerability detection, reasoning, and automated repair.

My broader goal is to develop trustworthy AI systems that can collaborate effectively with humans in high-stakes environments. Learn more →
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Recent Projects

RSAT

Structured attribution framework for training small LLMs to generate step-by-step reasoning with explicit citations, improving faithfulness and interpretability.

LLMs, Reasoning, Trustworthy AI, RL

Adaptive RAG

Reinforcement learning-based retrieval framework that learns when and how much to retrieve, improving efficiency and reasoning in LLM systems.

RAG, Reinforcement Learning, LLM Systems

VulnGraph

Graph + LLM framework combining structural and semantic reasoning for explainable vulnerability detection in source code.

GNNs, LLMs, Code Security, Explainable AI

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Recent Publications
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Experiences
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Contact
I'm open to research collaborations, PhD opportunities, and discussions on LLMs, reasoning, and trustworthy AI.
Email: jugal.gajjar@gwu.edu
Contact Page
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