We solicit proposals for tutorials for presentation at the 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA'2016). Topics can be in all areas of data science and advanced analytics, and duration can be either full-day (6 hrs) or half-day (3 hrs). The tutorial proposal should be no more 5 pages in length (+1 cover sheet), single- or double-column, and fonts should be 11pt or larger. The proposal should include the following:

• The proposed tutorial title.
• The preferred duration (3 or 6 hours). If both options are possible, the proposal should identify which material is to be included for each length.
• An abstract.
• A table of contents and enough detail to provide a sense of both the scope of material to be covered and the depth to which it will be covered.
• Specific goals and objectives.
• A short description of the intended audience and any prerequisite knowledge for attendees.
• A very brief biography of the proposed presenter(s).
• If (part of) the tutorial material or an earlier version of it has been presented elsewhere, the proposal should indicate those respective events (and dates), and describe how the current proposal differs from the previous editions.
• Audio Visual equipment needs for the presentation
• A cover sheet to each copy with the presenter's name, affiliation, address, email, and phone (not counted in the 5 pages).

Please submit your proposals via email to both of the Tutorial Co-Chairs:
Anastasia Ailamaki (natassa AT epfl DOT ch)
and
Ioana Manolescu (ioana DOT manolescu AT inria DOT fr)

Important Dates

May 30, 2016, 11:59pm PST: Proposal submission
June 30, 2016: Notification
September 30, 2016, 11:59pm PST: Camera-ready, tutorial slides and other materials due
October 17-19, 2016: Conference dates

We will consider all proposals for introductory and advanced tutorials on any Data Science and Analytics topic. Preference will be given to original tutorials which have not yet or recently presented elsewhere. Introductory tutorials should target an audience consisting of advanced undergraduate and graduate students, research scientists, as well as attendees from industry. Each accepted tutorial will receive one free registration to DSAA 2016. Presenters will be required to provide comprehensive tutorial notes (printable slides) one week before the event, which will then be published on the DSAA web site and thus be made available to conference attendees free of charge. Tutorials will be presented during the main DSAA conference as parallel sessions.

Suggested tutorial topics are as follows but not limited to:

• Foundations
  • New mathematical, probabilistic and statistical models and theories
  • New learning theories, models and systems
  • Deep analytics and learning
  • Distributed and parallel computing (cloud, map-reduce, etc.)
  • Non-iidness (heterogeneity & coupling) learning
  • Invisible structure, relation and distribution learning
  • Intent and insight learning
  • Scalable analysis and learning
  • Mining multi-source and mixed-source information
  • Architecture, management and process
  • Data pre-processing, sampling and reduction
  • Feature selection and feature transformation
  • High performance/parallel/distributed computing
  • Analytics architectures and infrastructure
  • Heterogeneous data/information integration
  • Crowdsourcing
  • Human-machine interaction and interfaces
• Retrieval, query and search
  • Web/social web/distributed search
  • Indexing and query processing
  • Information and knowledge retrieval
  • Personalized search and recommendation
  • Query languages and user interfaces
• Analytics, discovery and learning
  • Mixed-type data
  • Mixed-structure data
  • Big data modeling and analytics
  • Multimedia/stream/text/visual analytics
  • Coupling, link and graph mining
  • Personalization analytics and learning
  • Web/online/network mining and learning
  • Structure/group/community/network mining
  • Big data visualization analytics
  • Large scale optimization
• Privacy and security
  • Security, trust and risk in big data
  • Data integrity, matching and sharing
  • Privacy and protection standards and policies
  • Privacy preserving big data access/analytics
  • Social impact
• Evaluation, applications and tools
  • Data economy
  • Domain-specific applications
  • Quality assessment and interestingness metrics
  • Complexity, efficiency and scalability
  • Anomaly/fraud/exception/change/event/crisis analysis
  • Large-scale recommender and search systems
  • Big data representation and visualization
  • Post-processing and post-mining
  • Large Scale Application Case Studies
  • Online/business/government data analysis
  • Mobile analytics for handheld devices
  • Living analytics

Tutorial Chairs