معرفی
Huiping Cao is a Professor in the Department of Computer Science at New Mexico State University (NMSU). She holds a Ph.D. from The University of Hong Kong (2007), and bachelor’s and master’s degrees from Renmin University of China (1999 and 2002). Her expertise spans databases, deep learning, data mining, time series analysis, and graph mining. Her research focuses on developing computational methodologies to extract knowledge from complex data such as sequences and graphs, with applications in smart grids, animal and range sciences, agriculture, and high-performance computing.
Dr. Cao’s work emphasizes interdisciplinary applications, including real-time cattle monitoring using sensors (GPS and accelerometers) for disease detection, parturition prediction, and grazing behavior analysis. She has developed tools like GRAZETOOLS for livestock behavior analysis and contributed to precision ranching systems using LoRa-WAN sensors. Her methodological innovations include backbone index models for multi-cost road networks and federated learning frameworks to address data imbalance in distributed systems.
Her research has addressed challenges such as uncertainty quantification in time series analysis, probabilistic modeling for segmentation tasks, and sensor error evaluation. She collaborates across disciplines, integrating machine learning with environmental and agricultural domains. Dr. Cao’s contributions also include advancements in graph-based influence discovery, semantically annotated observational data systems, and compression techniques for power grid monitoring data.
