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KDD 2017 : Call for Tutorials


When Aug 13, 2017 - Aug 17, 2017
Where Halifax, Nova Scotia, Canada
Submission Deadline Feb 24, 2017
Notification Due Apr 7, 2017
Categories    knowledge discovery   data mining   data science   machine learning

Call For Papers

KDD 2017 Call for Tutorials

Continuing the tradition of bridging the gap between research and application, we are excited to call for participation in two flavors of tutorials at KDD 2017:
1. Traditional Tutorials
2. Hands-on Tutorials.

A Traditional Tutorial will cover the state-of-the-art research, development and applications in a specific data mining related area, and stimulate and facilitate future work. Tutorials on interdisciplinary directions, novel and fast growing directions, and significant applications are highly encouraged. We also encourage tutorials in areas that are somewhat different from the usual KDD mainstream, but still very much related to KDD mission and objectives of gaining insight from data.

A Hands-on Tutorial will feature in-depth hands-on training on cutting edge systems and tools of relevance to data mining and machine learning community. These sessions are targeted at novice as well as moderately skilled users. The focus should be on providing hands-on experience to the attendees. The pace of the tutorial should be set such that beginners can follow along comfortably. The tools & systems themselves must be available under open-source licenses (e.g. Apache 2.0, MIT, BSD etc.) and have proven track record of success in the community. Tutorials should introduce the motivation behind the tool, associated fundamental concepts, and work through examples and demonstrate its application to relatable real-life use cases.

All tutorials will be part of the main conference technical program, and are available free of charge to the attendees of the conference. Traditional tutorials will be of 3 hours in duration while Hands-on tutorials can be either 3 hours or 6 hours long.

We invite proposals from researchers, creators, experienced practitioners & tutors of data mining systems and tools. Each proposal must include details along following lines –
1. Title
2. Abstract (300 words)
3. Target audience and prerequisite for the tutorial (e.g. audience expertise)
4. Tutors (name, affiliation, email, address, phone)
5. Tutors’ short bio and their expertise related to the tutorial (up to 200 words per tutor)
6. Corresponding author with her/his email address
7. Tutorial outline. Provide as much detail as possible.
8. A list of forums and their time and locations if the tutorial or a similar/highly related tutorial has been presented by the same author(s) before, and highlight the similarity/difference between those and the one proposed for KDD’17 (up to 100 words for each entry)
9. A list of the most important references that will be covered in the tutorial
10. Equipment you will bring (e.g. laptop, projector)
11. Equipment you will need (e.g. table, poster board, power sockets)
12. Equipment attendees should bring (e.g. laptop)
13. Hands-on-Tutorial: Tutorial duration – 3 hours OR 6 hours?
14. Hands-on Tutorial: Operating system and required installed tools on attendees’ devices.
15. Hands-on Tutorial: List of software licenses required for the tools.
16. Hands-on Tutorial: Setup instructions for attendees. (Should not take more than 1 hour to complete it)
17. Selected tutorial must provide slides and related materials for tutorial website by the “Slides due” date below.
18. Optional: Video snippet of you teaching a tutorial or giving a talk.

Proposals should be received by Friday, Feb 24, 2017. Please submit proposals for traditional tutorials to and for hands-on tutorials to

* Tutorial proposal submissions: February 24, 2017
* Tutorial proposal notifications: April 7, 2017
* Slides due: July 31st, 2017

* KDD 2016 Traditional Tutorials:
* KDD 2016 Hands-On Tutorials:

* Yan Liu (University of Southern California)
* Jianyong Wang (Tsinghua University)
Contact email:

* Hanghang Tong (Arizona State University)
* Ritwik Kumar (Apple)
Contact email:

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