Tensor Decompositions for Big Multi-aspect Data Analytics

When:
Friday, February 2, 2018, 11:00 am - 12:00 pm PSTiCal
Where:
11th floor large conference room
This event is open to the public.
Type:
AI Seminar
Speaker:
Evangelos (Vagelis) Papalexakis, CSE UC RIverside
Video Recording:
https://bluejeans.com/154069839
Description:

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 such as the one of 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. 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 the scenario where the data are continuously updated over time. Finally, I will discuss open problems in unsupervised tensor mining and quality assessment of the results, and present work-in-progress addressing that problem with very encouraging results.

Evangelos (Vagelis) Papalexakis is an Assistant Professor of the CSE Dept. at University of California Riverside. He received his PhD degree at the School of Computer Science at Carnegie Mellon University (CMU). Prior to CMU, he obtained his Diploma and MSc in Electronic & Computer Engineering at the Technical University of Crete, in Greece.

Broadly, his research interests span the fields of Data Mining, Machine Learning, and Signal Processing. His research involves designing scalable algorithms for mining large multi-aspect datasets, with specific emphasis on tensor factorization models, and applying those algorithms to a variety of real world multi-aspect data problems. His work has appeared in KDD, ICDM, SDM, ECML-PKDD, WWW, PAKDD, ICDE, 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 during the summers of 2013 and 2014 and Google Research during the summer of 2015. Finally, his doctoral dissertation received the 2017 SIGKDD Doctoral Dissertation Award (runner up).

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