معرفی
Liu Yang is a Postdoctoral Fellow at the Aghaeepour Lab, focusing on data-driven neonatal research to enhance outcomes for premature infants. His work integrates artificial intelligence (AI) and machine learning (ML) with biomedical applications, including anomaly detection, imbalanced classification, and predictive modeling for fetal and cerebral health.
Education:
- Ph.D. in Electrical Engineering from Stony Brook University, New York, U.S.
- M.S. in Signal and Information Processing from Jiangnan University, China
- B.S. in Communications Engineering from Jiangnan University, China
- Visiting Graduate Student at the University of Missouri, Columbia, U.S.
His research trajectory began with statistical signal processing for target localization and tracking during his master's studies. During his Ph.D., he advanced nonparametric Gaussian process models for biomedical ML, particularly in predicting fetal well-being and simulating cerebral blood dynamics using arterial and intracranial pressure data. At the Aghaeepour Lab, he now analyzes bedside monitoring waveforms and electronic health records to uncover correlations with adverse neonatal conditions.
Liu Yang's intersection of artificial intelligence and medicine emphasizes early disease prediction and personalized interventions. His methodologies span anomaly detection, imbalanced classification, and circuit modeling for biomedical data analysis.
Outside academia, Liu Yang pursues classical piano, photography, painting, and traditional Chinese arts like calligraphy, seal carving, origami, and paper cutting. He occasionally shares piano performances via a personal YouTube channel.
