Directory profile for Dr. Subash Neupane

My research focuses on artificial intelligence, machine learning, natural language processing, and data science, with an emphasis on developing trustworthy, explainable, and clinically useful AI systems for healthcare and other high-impact applications. A central theme of my work is understanding how AI systems reason, make decisions, and interact with people, particularly in settings where reliability, transparency, safety, and human values are critical. My research explores large language models (LLMs), generative AI, agentic AI, neuro-symbolic AI, clinical reasoning, reasoning verification, human-centered AI, and multimodal learning. In healthcare, I am particularly interested in clinical decision support, patient-centered AI, care coordination and care transitions, medical conversation understanding, clinical summarization, patient education, and AI systems that incorporate patient preferences while maintaining evidence-based recommendations. My group also investigates natural language and speech technologies for low-resource languages, including automatic speech recognition and clinical NLP, as well as methods for evaluating the accuracy, robustness, fairness, and trustworthiness of AI-generated information. Current and emerging research topics include • Trustworthy and Explainable AI • Large Language Models and Generative AI • Agentic and Multi-Agent AI Systems • Neuro-Symbolic AI and Clinical Reasoning Verification • AI for Healthcare and Clinical Decision Support • Patient-Centered and Preference-Aware AI • Care Coordination and Care Transitions • Clinical NLP, Speech Recognition, and Summarization • Low-Resource Language Technologies • Human-AI Interaction and AI Evaluation. Our research group combines expertise in machine learning, NLP, software development, data analytics, and interdisciplinary healthcare research to build and evaluate practical AI systems and to train students to conduct rigorous, impactful, and responsible AI research.