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           ICCS2017-AMCMD Track - Workshop

                 "Applications of Matrix Computational Methods in the Analysis of  Modern Data" 

                                                         June 12-14, 2017,   Zürich, Switzerland




                                                                                                     ICCS 2017

                                         INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE

                                                                   Track - Workshop in

                Applications of Matrix Computational Methods in the Analysis of  "Modern Data"

                         


Objectives and Description of the Workshop:

“Modern Data” has unique characteristics such as, extreme sparsity, high correlation, high dimensionality and massive size. Modern data

is very prevalent in all different areas of science such as Medicine, Environment, Finance, Marketing, Vision, Imaging, Text, Web, etc. A 

major difficulty is that many of the old methods that have been developed for analyzing data during the last decades cannot be applied on

modern data. One distinct solution, to overcome this difficulty, is the application of matrix computation and factorization methods such as

SVD (singular value decomposition), PCA (principal component analysis), and NMF (non- negative matrix factorization), without which the

analysis of modern data is not possible. This workshop covers the application of matrix computational science techniques in dealing with

Modern Data.


Themes (not limited to)

Theoretical Aspects of “Modern Data”

Sparse Matrix Factorization

Recommender System

Dimension Reduction and Feature Learning

Deep Learning

Computational Cognition

Computational Finance

Image and Voice Recognition

Machine Learning Software Engineering

Singular Value Decomposition in “Modern Data”

Social Computing

Vision

NLP and  Text Analytics

Biostatistics and Computational Biology

Graph Algorithms


Contact

Track Chair and Organizer: Kourosh Modarresikouroshm@alumni.stanford.edu



Program Committee  

Ram Akella (UC Santa Cruz)

David Cavander (Adobe Inc.)

David Gal (UIC)

Christophe Giraud-Carrier (BYU)

Trevor Hastie (Stanford University)

Paul Hofmann (space-Time Insight) 

Pradeep Javangula (Adobe Inc.) 

Jeremy Kepner (MIT LL)

Tze L Lai (Stanford University)

Roy Lettieri (CPG IND)

Lexin Li (UC Berkeley)   

Akash Vivek Maharaj (Stanford)

Rahul Mazumder (MIT)

Kourosh Modarresi (Adobe Inc.)                              

James O'Keeffe (LiveSpace)

Hersir Sigurgeirsson (University of Iceland)

Bongwon Suh (Seoul National university)

Raja P Velu (Syracuse University)

Hongyuan Yuan (Adobe Inc)

Qi Zeng (Stanford)

Ji Zhu (University of Michigan)


Call for Paper Submissions

 Submit your paper via Easychair
Submission deadline is February 10th, 2017 . Please submit a short abstract now to indicate your interest.

Authors are invited to submit manuscripts reporting original, unpublished research and recent developments in Computational Sciences. 

All accepted full papers will be included in the open-access Procedia Computer Science series and indexed by Scopus, ScienceDirect, Thomson Reuters 

Conference Proceedings Citation (former ISI Proceedings) – an integrated index within Web of Science. The papers will contain linked references, XML 

versions and citable DOI numbers.

Please submit your paper via the conference website at Easychair . Do Not Forget to select the  "Applications of Matrix Computational 

Methods in the Analysis of Modern Data" track when submitting.


Important Dates

Paper submission 10th February, 2017
Notification of acceptance of papers 20th March, 2017
Camera-ready papers 5th April, 2017
Author registration 10th – 31st March, 2017
Participant (non-author) early registration 15th March – 5th April, 2017
Participant (non-author) late registration From 21st April, 2017
Welcome Reception To be defined
Conference sessions June 12 – 14, 2017