Brand new in KDD 2018, the Project Showcase Track offers a full day focused exclusively on innovative KDD-relevant projects from national and regional funding programs, as well as corporate, start-up, and nonprofit channels. We seek to bring together a diverse community of researchers in Machine Learning and Data Analytics, as well as partnerships in the social and physical sciences/arts, to show the state-of-the-art in research and applications.
We are soliciting submissions primarily from collaborative projects, whether publicly or privately funded. We will give preference to open innovation projects, especially those with contributions to open source, open data, or contributing to socially impactful goals. However, we welcome submissions from especially interesting projects that do not exactly fit these criteria if they provide new insights and compelling demonstrations.
Projects will be given 5-15 minute pitch/presentation slots, plus demonstration time, and a poster slot. All projects must provide instructive and compelling demonstrations, NOT marketing.
Submit extended abstracts (1-2 pages) to https://easychair.org/conferences/?conf=kdd18 Project Showcase Track:
- Brief description of the research project, goals, and partners;
- What is the main innovation that you would like to show?
- How does this fit with the KDD ecosystem?
- If available, provide links to online demonstrations (recommended), or a description of what you plan to show interactively to the audience;
- If available, provide links to project URL, repositories, and other dissemination materials you have available (optional);
Evaluation of projects will be based on the level of innovation and research breakthroughs, fit with KDD, how compelling the demonstrations/additional information are, and the need to create an event with a broad array of themes.
Submission Due Date: June 1, 2018
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KDD 2018 - London, United Kingdom. 19 - 23 August 2018
The annual KDD conference is the premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.
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