Hi,

I am Claire (Nayoung) Kim, a Computer Science Ph.D. candidate at the School of Computing and Augmented Intelligence (SCAI) at Arizona State University. I build and evaluate trustworthy NLP and LLM systems, with research spanning fairness, bias mitigation, hallucination, robustness, and human-centered AI evaluation.

My work is strongest at the intersection of rigorous research and practical AI systems: defining measurable failure modes, designing mitigation methods, running careful evaluations, and building usable ML/LLM prototypes.

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Research Focus

• Trustworthy LLMs: fairness, reasoning-time bias, hallucination mitigation, robustness, and model reliability.
• Evaluation and applied science: LLM-as-a-judge workflows, uncertainty-aware evaluation, human-subject studies, and statistical analysis.
• Agentic AI systems: RAG, multi-agent LLM systems, synthetic QA generation, PyTorch/Hugging Face experimentation, and deployment-oriented prototypes.