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CALL FOR ABSTRACTS

4th Swedish Workshop on Data Science (SweDS 2016)
Skovde, Sweden
November 10-11, 2016
http://www.mdai.cat/sweds16

SweDS-16, the fourth Swedish National Workshop on Data Science, brings together researchers, practitioners, and opinion leaders with interest in data science. The goal is to further establish this important area of research and application in Sweden, foster the exchange of ideas, and to promote collaboration. As a followup to the very successful previous editions, held at the University of Borås, Stockholm University, and Blekinge Institute of Technology we plan two full days of inspiring talks, discussion sessions, a student forum, and time for networking. We invite stakeholders from academia, industry, and society to share their thoughts, experiences and needs related to data science. The workshop is organised by the Skovde Artificial Intelligence Lab (SAIL) at the University of Skovde.

Contact: Vicenc Torra (SweDS 2016 chair) and the SAIL team at sweds16@his.se.

* Abstract submission

We invite academic researchers as well as industrial researchers and practitioners to submit short abstracts (150-500 words) in one of the following categories: (1) original research, (2) new/relevant challenge, (3) status report of ongoing work. Abstracts will be screened and selected based on relevance and quality. The authors of accepted abstracts are given approx. 20-30 minutes to present (incl. questions/discussion). 

Submit your abstracts to sweds16@his.se.
The email should include the following 
- Email subject: SweDS 2016
- Title of the talk
- Authors
- Affiliations
- Abstract (in plain text in the body of the message, use latex commands if formulas are needed) 
- Reference(s). If the paper has already been published, include full reference of the publication.

* Important dates

    Abstract submission deadline: Sep 8, 2016
    Notification to authors/presenters: Sep 10, 2016
    Workshop registration: Sep 16, 2016
    Workshop: Nov 10 - 11, 2016

* Topics: 

Data science focuses on the extraction of knowledge from data. The overall aim is to make better use of the ever increasing amount of data generated by individuals, societies, companies, and science. To achieve this aim, the objectives are to identify relevant challenges and problems, to study, develop and evaluate solutions based on efficiency and effectiveness, and to perform successful implementations. Data science is based on theory and methods from many fields, including: computer vision, data mining & knowledge discovery, machine learning, optimization, statistics, and visualization.

Data science is not about blindly sifting through data in the hope for interesting results and discoveries. In contrast, data science requires the ability to make sense of our complex world and the domain under study, and to use this understanding and the available data to develop suitable mathematical models that help us explain and predict interesting phenomena.

* Topics of Interest. They include, but are not limited to, the following

** Methods and Algorithms

    Classification, Clustering, and Regression
    Probabilistic & Statistical Methods
    Graphical Models
    Spatial & Temporal Mining
    Data Stream Mining
    Feature Extraction, Selection and Dimension Reduction
    Data Cleaning, Transformation & Preprocessing
    Multi-Task, Multi-label, and Multi-output Learning
    Big Data, Scalable & High-Performance Computing Techniques
    Mining Semi-Structured or Unstructured Data
    Text & Web Mining
    Data privacy

** Applications

    Image Analysis, Restoration, and Search
    Climate/Ecological/Environmental Science
    Risk Management and Customer Relationship Management
    Genomics & Bioinformatics
    Medicine, Drug Discovery, and Healthcare Management
    Automation & Process Control
    Logistics Management and Supply Chain Management
    Sensor Network Applications and Social Network Analysis