Call for Main Track Papers
Call for Main 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 to design, implementation and results of solutions for application of data science techniques to real-world problems. 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 both for novelty of the described approaches as well as suitability for real-world applications. 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. Papers accepted in the non-archival track will be presented in the conference but not published. 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; AI/ML for biology
- 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.
- Deployments and Lessons Learned: Deployed experience papers from industry, government agencies, startups and NGOs relating to the large-scale deployment of data science applications and operations (MLOps, DLOps). Subtopics include 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.
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.
One paper, picked by a jury comprising members from both the external research community and the program committee, will be awarded the best technical paper award. Partial travel Grants will be available for students whose papers are accepted.
Important Dates
All deadlines are Anywhere on Earth (AoE, UTC-1200)- August 13, 2026: Submission of papers
- October 6, 2026: Final decision notifications (Accept / Reject)
- November 18, 2026: Camera Ready Due
Submission Link
https://openreview.net/group?id=ACM.org/IKDD/CODS/2026/ConferenceProgram Chairs
- Sriparna Saha, IIT Patna
- Vishwa Vinay, Canva
- Sandipan Dandapat, IIT Hyderabad
- Srikrishna Karanam, Adobe Research
For more details, please reach out to the chairs at cods@acmindia.org