The Applied Data Sciences (ADS) track invites both full (8 pages + unlimited references) as well as short (4 pages + unlimited references) papers describing the design, implementation and results of solutions and systems for application of data science techniques to real-world problems.
The review process will take place in two stages.
All accepted papers will get the opportunity to present their work in the conference. Accepted papers will appear in the proceedings of the conference, which will be published in ACM Digital Library.
Topics of interest include, but are not limited to the following:
Authors are strongly encouraged to make their code and data publicly accessible during the review process, unless there is an inevitable reason that prohibits sharing (e.g., it requires data from a specific company or it is medical data where there is no public alternative). Algorithms and resources used in a paper should be described as completely as possible to enable reproducibility. This includes model parameters, experimental methodology, hardware and software platforms used during empirical evaluations, and results. The reproducibility factor will play an important role in the assessment of each submission. In the case where data cannot be released publicly, authors are encouraged to include experiments on relevant public datasets and/or create simulated data with the same properties.
Additionally, please see this page to help you decide between the Research Track and Applied Data Science Track.
Please read the Dual submission, Plagiarism and Conflict of Interest policies before finalising your submission.
Several technical awards are available for best paper, etc. Please see the Awards page for details.
Partial travel Grants will be available for students (both domestic and international) whose papers are accepted. Note that each paper must be presented in-person.
All deadlines are Anywhere on Earth (AoE, UTC-1200)
Please see this page for submission instructions. Note that unlike research track papers, ADS track papers may be submitted in either single or double-anonymous mode, i.e., listing author information is left to the discretion of the authors.
Sayan Ranu, IIT Delhi
Kalika Bali, Microsoft Research, India
For more details, please reach out to the track chairs at [email protected]
Please see this page for program committee members.