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Objectives and Description of the Workshop:

With availability to large amount of data, the main challenge of our time is to get insightful information from the data. Therefore, artificial intelligence and machine learning are two main paths in getting the insights from the data we are dealing with. The data we currently have is a new and unprecedented form of data, "Modern Data". “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 AI and Machine Learning

Interpreting Black Box models in AI, ML

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 Modarresi, kouroshm@alumni.stanford.edu

Session Co-Chairs of the meeting: Wenpeng Liu, liuwenpeng@iie.ac.cn and Ke Du, ke.du@utbm.fr

Program Committee

Mehrdad Aghamohammadi (The University of Auckland)

Ram Akella (UC Santa Cruz)

David Cavander (Adobe Inc.)

David Gal (UIC)

Walter Gander (ETH)

Trevor Hastie (Stanford University)

Paul Hofmann (Accenture)

Jeremy Kepner (MIT LL)

Tze L Lai (Stanford University)

Roy Lettieri (CPG IND)

Lexin Li (UC Berkeley)

I-Jong Lin (Adobe)

Rahul Mazumder (MIT)

Kourosh Modarresi (Adobe Inc.)

Hersir Sigurgeirsson (University of Iceland)

Bongwon Suh (Seoul National university)

Ka Wai Tsang (The Chinese University of Hong Kong)

Raja P Velu (Syracuse University) -- Meeting Session Chair

Zepu Zhang (Ad & Data)

Ji Zhu (University of Michigan)

Call for Paper Submissions

Submit your paper via Easychair

Submission deadline is February 15, 2018 . 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.

The manuscripts of up to 10 pages, written in English and formatted according to the Springer LNCS templates, should be submitted electronically via EasyChair. Templates are available for download in EasyChair horizontal menu “Templates”.

Papers must be based on unpublished original work and must be submitted to ICCS only. Submission implies the willingness of at least one of the authors to register and present the paper.

During submission, you may select either a “Full Paper” or a “Abstract Only” publication. By default, it would be an oral presentation. If you prefer to present a poster, please check the “Poster Presentation” option in the submission page.

While we encourage full paper submissions, the “Abstract Only” option caters to researchers who can only publish in specific journals or work for companies in circumstances such that they cannot publish at all, but still want to present their work and discuss it with their peers at ICCS. In the “Abstract Only” option, a short abstract is published in a book of abstracts, but not in the Procedia Computer Science

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 Methods In Artificial Intelligence and Machine Learning" track when submitting.

Important Dates