6月20日上午，应理学院邀请美国蒙大拿州立大学秦菁教授在文渊楼理学院会议室做了一场题为“ Graph Regularizations in EEG Source Localization”(图形正则化在脑电源定位中的应用)的学术报告。并与来自信息与计算科学、数学与应用数学、统计学等相关专业的教师和学生代表进行了学术交流，学术交流会由吕小光副院长主持。

    秦菁教授于2013年在凯斯西储大学获得应用数学博士学位，之后在洛杉矶加州大学担任助理副教授三年。自2016年夏以来，她一直在蒙大拿州立大学数学系担任助理教授。她的研究兴趣包括变分图像处理/分析及其应用，基于压缩传感的图像重建，数值优化和应用偏微分方程。

在学术报告中，秦菁教授结合自身的研究成果与我院师生分享图形正则化在脑电源定位中的应用，并为大家介绍了所在学校的专业及地理位置，以及博硕士学业申请出国留学等相关经验分享和建议。此次报告会让在场的师生得以拓宽知识面、充实知识储备，收获颇丰。

附报告会相关信息

报告题目：Graph Regularizations in EEG Source Localization

报 告 人：Qin Jing 博士

报告时间：2018年6月20日（周三）上午09：40-11：00

报告地点：理学院大会议

摘要: Electroencephalogram (EEG) serves as an essential tool for brain source localization due to its high temporal resolution. However, the inference of brain activities from the EEG data is a challenging ill-posed inverse problem. In this talk, we investigate several EEG source localization methods based on various graph regularizations, including graph total generalized variation, graph fractional-order total variation, and temporal graph regularization. Numerical results have shown that the proposed methods localize source extents more effectively than the benchmark methods.

个人简介：Jing Qin earned her Ph.D. in applied mathematics from Case Western Reserve University in 2013, and then worked as an assistant adjunct professor at the University of California, Los Angeles for three years. Since summer 2016, she joined the Department of Mathematical Sciences at Montana State University as an assistant professor. Her research interests include variational image processing/analysis and its applications, compressive sensing based image reconstruction, and numerical optimization and applied partial differential equations.