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                                                                       ICCS 2020

                                         INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE

                                                                              Track in

                                    Applications of Computational Methods in Artificial Intelligence and Machine Learning  

                                                                   June 3-5, 2020, Amsterdam, The Netherlands



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Objectives and Description of the Workshop:

Our time could be defined as the age of Data. With the availability of large amount of data and massive computational resources, the main challenge before data scientists is to get insightful information from the data. Naturally,  AI (Artificial Intelligence) and ML (Machine Learning) are two main vehicles in getting the insights. The main type of  recently available  data  is indeed  a new, modern and unprecedented form of 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 in dealing with modern data is that many of the old methods that have been developed for analyzing data during the last decades cannot be applied directly to modern data. One major solution, to overcome this challenge, is to effectively use ensemble methods such as deep  artificial neural networks. These types of ensemble models are heavily reliant on the deployment of efficient computational methods. Thus, it's even more imperative to deploy  faster, more accurate and robust computational techniques for AI and ML models.

This track covers the application of computational methods for Artificial Intelligence and Machine Learning models.


Themes (not limited to)

Theoretical Aspects of AI and Machine Learning

History of Computational Methods in AI and ML

Deep Learning

Auto ML

Computer Vision

Video-to-Video Synthesis

NLP and  NLU (Text Analytics)

Automatic Speech Recognition (ASR)

Matrix Factorization Methods

Recommender Systems

Computational Cognition

Computational Finance

Singular Value Decomposition in “Modern Data”

Dimension Reduction and Feature Learning

AI Conversational Assistant

Biostatistics and Computational Biology


Contact

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

Co-chairs:

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

Paul Hofmann (Accenture) -- Meeting Session Chair


Program Committee  

Ram Akella (UC Santa Cruz)

David Gal (UIC)

Walter Gander (ETH)

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)

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 7, 2020. 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 from this link.

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.

Deadlines for draft paper submission, notification of acceptance, camera-ready paper submission and registration may be found in the Important Dates section.


You are welcome to participate in one of the thematic tracks or in the Main Track (if your topic does do fit any thematic track but still falls within the scope of the conference). 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 Computational Methods in Artificial Intelligence and Machine Learningtrack when submitting.


Important Dates

Paper submissionFebruary 7, 2020 (Final Extension)
Notification of acceptance of papersMarch 12, 2020
Camera-ready papersApril  2 , 2020
Author registrationMarch 12- April  2,  2020
Participant (non-author) early registrationMarch 12 - April 2,  2020
Participant (non-author) late registrationFrom  April 3, 2020
Welcome ReceptionJune 3, 2020
Conference sessionsJune 3-5, 2020