报告题目：Connectivity inference by asynchronously updated kinetic Ising model (利用异步更新的动态伊辛模型重建网络结构)

　　报告人：曾红丽

　　时间：2014年12月24日14：30

　　地点：三牌楼校区科研楼712

　　主办单位：电子科学与工程学院、科技处

　　报告人简介：

　　曾红丽，于芬兰阿尔托大学（Aalto University）获物理学博士学位，现为瑞典乌普萨拉大学埃格斯特朗实验室（Angstrom Laboratory, Uppsala University）博士后研究员。曾入选芬兰计算科学博士培养计划FICS成员，多次受邀到波尔研究所（Niels Bohr Institute，Denmark）、北欧理论物理学会（Nordic Institute for Theoretical Physics (Nordita)，Sweden）、挪威科技大学（Norwegian University of Science and Technology (NTNU) ，Norway）开展合作研究。在Phys. Rev. Lett.、 Phys. Rev. E等国际著名物理学、交叉科学刊物上发表学术论文。目前研究方向主要集中于inference problem（反推问题）、stochastic process（随机过程）、statistical machine learning （统计机器学习）、Patter analysis（图像处理）等领域。

　　报告简介：

　　The talk focuses on the inference of network connections from statistical physics point of view. The reconstruction methods of the asynchronously updated kinetic Ising model with an asymmetric Sherrington-Kirkpatrick (SK) model are studied theoretically. Both approximate and exact learning rules for the couplings from the generated dynamical data are developed. All the learning rules are studied numerically. Good convergence is observed in accordance with the theoretical expectations. Several of the derived algorithms are applied to real experimental neural and financial dataset. Meaningful results are produced in both cases.