报告题目：Weakly supervised learning and its applications in remote sensing and digital health
报告摘要：Sensors are regarded as the “electronic sensory organ in information era”, which has changed the live style of humans. Remote Sensing and Digital Health are two typical applications of them, and these sensor systems are yielding massive data each day. How to effectively analyze the data is the key problem for the applications. In recent years, artificial intelligence has become the most promising research topic for data analysis in these two fields. However, there are still some challenges confronting the tasks: i) Label scarcity. ii) Heavy noise. iii) Data variability and heterogeneity. Here, I will present several works from me which address these issues. Different machine learning paradigms, including semi-supervised learning-based, active learning-based and Markov random field-based approaches are designed and applied. The findings from these works point out that making full use of the unlabeled data and the prior knowledge are effective ways to improve the prediction performance and reduce the annotation cost.
报告人简介：毕海霞，西安交通大学“青年拔尖人才计划”入选者，西安交通大学电信学部特聘研究员，博士生导师。2003年和2006于中国海洋大学分别获得学士和硕士学位；之后于华为和爱立信从事软件研发工作；2018年于西安交通大学获得博士学位；2018至2021年于英国德比大学和布里斯托大学从事博士后研究工作。主要研究领域为机器学习算法研究及其在遥感和健康医疗领域的应用。在IEEE Trans. Image Processing、IEEE Trans. Geoscience and Remote Sensing、IEEE Journal of Biomedical and Health Informatics等国际权威学术期刊和IGARSS等国际会议发表论文30余篇。于2020年获得了IEEE Geoscience and Remote Sensing Letters的Best Reviewer奖项(全球共5人)。