Call for Research Track Papers

We invite the submission of papers describing innovative and original research contributions in the areas of data science, data management, data mining, machine learning, and artificial intelligence, as well as papers describing the design, implementation and results of solutions of such advances to real-world problems. Papers can range from theoretical contributions to systems and algorithms to experimental research and benchmarking. We invite two types of submissions:

  • Archival papers: 8 pages + 2 pages for references.
  • Non-archival papers: 2 pages + 1 page references.

Papers submitted to the Archival track will undergo a rigorous review process that will judge submissions for the novelty of the described approaches. The goal of the non-archival track is to provide authors with a fast-track route to get feedback for any preliminary ideas that show promising results but do not yet have the maturity to be submitted to the archival track.

Papers accepted in the archival track will appear in the conference proceedings and be published. The publication venue will be announced soon. Papers accepted in the non-archival track will be presented in the conference but not published.

Unlike previous years, we will have a single-stage review process this time, i.e., there will not be a rebuttal stage and no option to submit revised papers. Authors of all accepted papers (archival and non-archival) must present their work at the conference.

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

  • AI, ML, and Data Mining: Classification and regression; Knowledge discovery; knowledge representation and knowledge-based systems; data preprocessing and wrangling; feature engineering; reinforcement learning; deep learning; Bayesian methods; time series analysis; optimization; graphical models; statistical relational learning; matrix and tensor methods; parallel and distributed learning; semi- and unsupervised learning; graph mining; network analytics; text analytics and NLP; information retrieval; learning-based computer vision; multimodal learning and analytics; human-in-the-loop learning; planning and reasoning; ML for mobiles and other resource-constrained environments; federated learning; AutoML; causality; weak supervision and data augmentation; new benchmark tasks and datasets for AI/ML/data mining.
  • Data Management: Data management systems (subtopics including but not limited to benchmarking, monitoring, testing, and tuning database systems, cloud, distributed, decentralized and parallel data management, database systems on emerging hardware, embedded databases, IoT and Sensor networks, Storage, indexing, and physical database design, Query processing and optimization, Transaction processing, Data warehousing, OLAP, Analytics); Models and Languages (subtopics including but not limited to Data models and semantics, Declarative programming languages and optimization, Spatial and temporal data management, Graphs, social networks, web data, and semantic web, Multimedia and information retrieval, Uncertain, probabilistic, and approximate databases, Streams and complex event processing); Human-Centric Data Management (subtopics including but not limited to Data exploration, visualization, query languages, and user interfaces, Crowdsourced and collaborative data management, User-centric and human-in-the-loop data management, Natural language processing for databases); Data Governance (subtopics including but not limited to Data provenance and workflows, Data integration, information extraction, and schema matching, Data quality, data cleaning, Data security, privacy, and access control, Responsible data management and data fairness, Metadata Management).
  • Intersection of AI & Data Management: Structured queries over unstructured data: images, video, natural language, natural language queries; machine learning methods for database engine internals; machine learning methods for database tuning; data management and metadata for machine learning pipelines; knowledge base management.
  • Data Science Ethics: Subtopics including, but not limited to quantifying and mitigating fairness and bias issues; improving model trust, transparency and explainability; data privacy; model alignment; environmental costs; governance and regulation.

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.

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 whose papers are accepted.

Double Anonymity Requirement

CODS 2025 will be using double-anonymous reviewing for Research Track papers (but not the ADS Track papers). Please review the instructions below carefully.

Authors’ names and affiliations must not appear on the title page or anywhere else in the submission. Funding sources must not be acknowledged anywhere in the submission. Research group members, or other colleagues or collaborators, must not be acknowledged anywhere in the submission. The file names of any documents submitted must not identify the authors of the submission. Source file naming must also be done with care, to avoid identifying the authors’ names in the submission’s associated metadata. Only after acceptance at the camera-ready stage should the author list, acknowledgments, and funding sources be added to the paper.

If a version of a submission already resides on a pre-publication server, such as arXiv, the authors do not need to remove it before submitting to CODS-COMAD.

Be careful when referring to related past work, particularly your own, in the paper. Authors must refer to their own past work in the third person. This allows setting the context for your submission, while at the same time preserving anonymity. Do not omit referring to your own past related work because that could reveal your identity by negation. Limit self-references to only the essential ones. Extended versions of the submitted paper (e.g., technical reports or URLs for downloadable versions) must not be referenced. Many peer-reviewed conferences have successfully followed double anonymity for decades to offer more equity for all authors in the reviewing process. Common sense and careful writing can go a long way toward preserving double anonymity without diminishing the quality or impact of a paper. It is the responsibility of the authors to do their very best to preserve double anonymity.

Papers that do not follow the guidelines here, or otherwise potentially reveal the identity of the authors, are subject to Desk Rejection.

Important dates

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

  • August 1, 2025: Submission of papers
  • September 22, 2025: Final decision notifications (Accept / Reject)
  • November 18, 2025: Camera Ready Due

Submission Instructions

Please see this page for submission instructions.

Program Chairs

Jana Doppa, Washington State University
Sriparna Saha, IIT Patna
For more details, please reach out to the chairs at cods@acmindia.org

Program Committee

Please see this page for program committee members.