Licong Cui, Ph.D. is an associate professor in McWilliams School of Biomedical Informatics at the University of Texas Health Science Center at Houston (UTHealth Houston). Dr. Cui's primary research goal is to develop innovative informatics methods and tools to address data science challenges in biomedicine. Dr. Cui’s research interests include ontologies and terminologies, neuroinformatics, big data analytics, large-scale data integration and management, information extraction and information retrieval. Dr. Cui has published over 100 peer-reviewed research papers and served as the principal investigator for a number of grants funded by NIH and NSF. Dr. Cui was the 2022 recipient of the prestigious American Medical Informatics Association (AMIA) New Investigator Award.
As a well-trained Computer Scientist specialized in Biomedical Informatics, Dr. Cui has strong grounding in algorithms and computational methodologies. For instance, she has designed and developed scalable algorithms for analyzing biomedical data, including extracting epilepsy phenotypes from clinical narratives for patient cohort identification, computing non-lattice subgraphs and detecting relation reversals in the SNOMED CT, mining lexical patterns in non-lattice subgraphs for detecting missing hierarchical relations and concepts in SNOMED CT, Gene Ontology and NCI Thesaurus, performing cross-ontology hierarchical relation examination in the Unified Medical Language System, and mining diverse clinical datasets from the National Sleep Research Resource. At the same time, Dr. Cui has considerable experience in applied informatics, particularly the lifecycle of web-based software development. For example, she has designed, developed and deployed a cross-cohort query system for the National Sleep Research Resource and a data integration system for Sudden Unexpected Death in Epilepsy research. Dr. Cui has conducted collaborative work in multidisciplinary team settings with sleep researchers, epilepsy researchers, cancer researchers, and other informaticians.
“As increasingly large amounts of digital data have been produced by the biomedical research community, ontologies and terminologies have been widely used for orchestrating the coding, management, exchange, and sharing of biomedical data. My research interest spans from the theoretical and computational aspects for analyzing biomedical ontologies (e.g., ontology quality assurance) to the application of ontologies to solve large-scale data science problems in biomedicine (e.g., information extraction and retrieval, data integration and management, and data mining). I am passionate at conducting data science related research in collaboration with biomedical and clinical domain experts.”