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Objectives and Description of the Workshop:

With the availability of 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 main type of recently available data is indeed 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

Co-chairs:

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

Michael Burkhart (Adobe) -- Meeting Session Chair

Program Committee

Ram Akella (UC Santa Cruz)

David Gal (UIC)

Walter Gander (ETH)

Christopher Giraud-Carrier (BYU)

Paul Hofmann (Accenture)

Julie Josse (argocampuss)

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.)

Apaar Sadhwani (Google)

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 (Walmart lab)

Ji Zhu (University of Michigan)

Call for Paper Submissions

Submit your paper via Easychair

Submission deadline is February 15, 2019 . 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 papers will be included in the Springer Lecture Notes in Computer Science (LNCS) series and indexed by Scopus, EI Engineering Index, Thomson Reuters Conference Proceedings Citation Index (included in ISI Web of Science), and several other indexing services. The papers will contain linked references, XML versions and citable DOI numbers."

"The manuscripts of up to 14 pages, written in English and formatted according to the Springer LNCS templates, should be submitted electronically via EasyChair. You also have the option of submitting a short paper of up to 7 pages. Both Full and Short Papers use the same templates and are published in LNCS. Templates are available for download in EasyChair’s “Templates” menu."

"During submission, you may select either a “Full/Short 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 LNCS."

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