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

Speaker:        Dr. Evangelos Papalexakis
                University of California Riverside

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

Date:           Thursday, 24 January 2019

Time:           11:00am - 12 noon

Venue:          Room 4472 (via lift 25/26), HKUST

Abstract:

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.


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Biography:

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).