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　　報告題目：Connectivity inference by asynchronously updated kinetic Ising model (利用異步更新的動態伊辛模型重建網絡結構)

　　報告人：曾紅麗

　　時間：2014年12月24日14：30

　　地點：三牌樓校區科研樓712

　　主辦單位：電子科學與工程學院、科技處

　　報告人簡介：

　　曾紅麗，于芬蘭阿爾托大學（Aalto University）獲物理學博士學位，現為瑞典烏普薩拉大學埃格斯特朗實驗室（Angstrom Laboratory, Uppsala University）博士后研究員。曾入選芬蘭計算科學博士培養計劃FICS成員，多次受邀到波爾研究所（Niels Bohr Institute，Denmark）、北歐理論物理學會（Nordic Institute for Theoretical Physics (Nordita)，Sweden）、挪威科技大學（Norwegian University of Science and Technology (NTNU) ，Norway）開展合作研究。在Phys. Rev. Lett.、 Phys. Rev. E等國際著名物理學、交叉科學刊物上發表學術論文。目前研究方向主要集中于inference problem（反推問題）、stochastic process（隨機過程）、statistical machine learning （統計機器學習）、Patter analysis（圖像處理）等領域。

　　報告簡介：

　　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.