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加州大學河濱分校Evangelos E. Papalexakis助理教授學術報告

發布:2019-01-17 15:00?????? 作者:admin??????來源:未知

報告題目:Tensor Decompositions for Big Multi-aspect Data with Applications to Misinformation on the Web and Social Graph Analytics

報 告 人:加州大學河濱分?!?/span>Evangelos E. Papalexakis   助理教授

報告時間:2019年1月17日(周四)15:00

報告地點:清水河校區主樓A1-513

邀 請 人:趙熙樂 研究員

 

報告摘要:

    Tensors and tensor decompositions have been very popular and effective tools for analyzing multi-aspect data in a wide variety of fields, ranging from Psychology to Chemometrics, and from Signal Processing to Data Mining and Machine Learning.  Using tensors in the era of big data presents us with a rich variety of applications, but also poses great challenges, especially when it comes to scalability and efficiency.

In this talk, I will first motivate the effectiveness of tensor decompositions as data analytic tools in a variety of exciting, real-world applications, including Misinformation on the Web and Social Graph Analysis. Subsequently, I will discuss recent techniques on tackling the scalability and efficiency challenges by parallelizing and speeding up tensor decompositions, especially for very sparse datasets, including streaming scenarios where the data are continuously updated over time.

Finally, I will discuss future directions in using tensor methods for characterizing and understanding deep neural networks and present encouraging preliminary results.

 

報告人簡介:

Evangelos E. Papalexakis is an Assistant Professor of the CSE Dept. at University of California Riverside. Broadly, his research interests span the fields of Data Mining, Machine Learning, and Signal Processing. His work has appeared in SDM, WWW, PAKDD, ICASSP, IEEE Transactions of Signal Processing, and ACM TKDD. He has a best student paper award at PAKDD’14 and SDM’16, finalist best papers for SDM'14 and ASONAM'13 and he was a finalist for the Microsoft PhD Fellowship and the Facebook PhD Fellowship. Besides his academic experience, he has industrial research experience working at Microsoft Research Silicon Valley and Google Research. Finally, his doctoral dissertation received the 2017 SIGKDD Doctoral Dissertation Award (runner up).


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