Call for Applied Data Science Track Papers

Applied Data Sciences Track

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.

  • In the first stage of the review, papers will be grouped as Accept, Major Revision or Reject.
  • In the second stage of the review, authors can revise and resubmit Major Revision papers. They will then be regrouped as Accept or Reject.

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 (Approval pending).

Topics of interest include, but are not limited to the following:

  • Novel combination of data science applications in domains such as education, software engineering, cloud computing, agriculture, transportation, energy, real estate, manufacturing, finance, retail, healthcare, e-commerce, digital marketing, telecommunications, social media and computational advertising, public policy, bio-chemical engineering, pollution tracking and climate change, material science, AI for natural sciences, bioinformatics, etc.
  • Deployed experience papers from industry, government agencies, startups and NGOs relating to the large-scale deployment of data science applications. Of particular interest are papers addressing issues relating to infrastructure for scale, ease of adoption, and new data science/management technologies. Papers should highlight pain points and new challenges emerging due to deployment of these new technologies. Verifiable evidence of business impact, social impact or other real-world impact from such deployments are encouraged.
  • Data sets: Applied scientific work on handling large or complex data sets from specific domains (e.g., noisy/incomplete medical data, judicial records, etc.).
  • Ethical issues in data science applications, fairness and bias, trust, data privacy, explainability, etc., especially when these issues are considered in relation to deployed systems.

Sharing and Reproducibility

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.

Important dates

All deadlines are Anywhere on Earth (AoE, UTC-1200)

  • July 10, 2023: Abstract submission
  • July 17, 2023: Submission of papers
  • September 11, 2023: First Stage Accept/Major Revision/ Reject decisions.
  • October 11, 2023: Re-submission of revised papers
  • November 3, 2023: Final notification of Accept/Reject decisions
  • November 30, 2023: Camera ready due

Submission Instructions

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.

Applied Data Sciences Track Chairs

Sayan Ranu, IIT Delhi
Kalika Bali, Microsoft Research, India
For more details, please reach out to the track chairs at [email protected]

Program Committee

Please see this page for program committee members.