Huan He

Dr. Huan He serves as a Research Scientist in the Section of Biomedical Informatics and Data Science at Yale University School of Medicine. Previously, he was a Research Fellow in the Department of Artificial Intelligence and Informatics at Mayo Clinic Rochester. His academic foundation includes a PhD in Computer Science from Xi'an Jiaotong University. Dr. He is actively engaged with the Clinical NLP Lab at Yale, contributing to cutting-edge research at the intersection of computer science and healthcare.

Dr. He's research primarily focuses on visual analytics applications in healthcare, with particular emphasis on natural language processing and machine learning technologies. His work centers on designing and developing visual analytics systems that facilitate data exploration for health-related clinical questions. He has made significant contributions to living evidence synthesis projects, establishing informatics infrastructure for systematic reviews and meta-analyses through data visualization techniques. His research bridges technical innovation with practical healthcare applications, particularly in the areas of clinical decision support and medical data analysis.

Analysis of Dr. He's publication record reveals a consistent trajectory in biomedical informatics with increasing focus on large-scale clinical data analysis. His recent work demonstrates growing expertise in applying large language models to medical literature synthesis, developing NLP frameworks for EHR analysis, and creating visualization tools for clinical decision support. The publications show a strong interdisciplinary approach, connecting computer science methodologies with real-world medical challenges, particularly in oncology and pandemic response.

Dr. He actively contributes to the academic community through service as a regular reviewer for prestigious journals including JBI, JMIR, TVCJ, and conferences such as IEEE VIS, IEEE ICHI, IEEE BIBM, AAAI, and AMIA. He has also been involved in organizing committees for VAHC workshops and IEEE ICHI conferences, demonstrating leadership within the medical informatics community.

His work extends to collaborative projects across institutions, particularly evident in his contributions to the National COVID Cohort Collaborative (N3C) and development of the OHNLP text annotation toolkit. These projects highlight his commitment to creating privacy-preserving tools for corpus development through innovative serverless architecture approaches. Dr. He's research demonstrates both technical sophistication and practical relevance to contemporary healthcare challenges.