LLM Reasoning & Code Generation

My research on LLMs spans reasoning evaluation, automated code/test generation, and reliability. Key contributions include:

• AgentTester — A multi-agent framework for automated unit test generation that significantly improves software reliability (AIWare 2025).
• HPCAgentTester — Extending multi-agent test generation to high-performance computing environments.
• LogBabylon — A unified framework for cross-log file integration and analysis using LLMs.
• Hallucination Mitigation — Techniques for reducing AI hallucinations in diagnostic and clinical contexts.
• Evaluating LLM Rationality & Randomness — Benchmarking the quality of reasoning and entropy in LLM-supported tasks.
• Synthesizing Public Opinions — Using LLMs to synthesize democratic discourse (IEEE ICEDEG 2025).

This line of research explores how multi-agent systems can produce more reliable, verifiable AI outputs, with applications from software engineering to healthcare.