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. Technical approaches can include data science, data mining, applied machine learning, testing and governance of data science models and solutions, and practical MLOps approaches.
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.
Accepted papers will be given the opportunity to present their work as an oral presentation. 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:
- 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, etc.
- Data Science, Machine Learning and AI: Data discovery, data preprocessing and wrangling, Classification and regression, parallel and distributed learning, semi- and unsupervised learning, matrix and tensor methods, graph mining, network analytics, reinforcement learning, feature engineering, deep learning, Bayesian methods, time series analysis, optimization, graphical models, relational models, text analytics and NLP, information retrieval, knowledge representation, knowledge-based systems, human-in-the-loop learning, planning and reasoning, ML for mobiles and other resource constrained environments, data mining, causality, fairness, accountability and transparency, interpretability, data visualisation.
- 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.
- 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: To enable reproducibility and data reuse, authors are encouraged to share artefacts including software, algorithms, protocols, code, datasets and other useful materials related to the research.
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. Presenters will also have the option to present the papers virtually.
|All deadlines are Anywhere on Earth (AoE, UTC-1200)|
July 03, 2022 July 10, 2022 : Abstract submission
July 10, 2022 July 17, 2022 : Submission of papers
|September 11, 2022: First Stage Accept/Major Revision/ Reject decisions.|
|October 9, 2022: Re-submission of revised manuscripts.|
|October 30, 2022: Final Notifications of Accept / Reject decisions.|
|November 15, 2022: Camera ready due|
Please see this page for submission instructions
Applied Data Sciences Track Chairs
- Tanuja Ganu, Microsoft Research India
- Ponnurangam Kumaraguru (PK), IIIT Hyderabad, India
- For more details, please reach out to the track chairs at [email protected]
Please see this page for program committee members.