[hpc-announce] [Deadline Extended] Call for Papers – DRAI 2025 Data Readiness for AI Workshop (in conjunction with ICPP 2025)

[Jean Luca Bez]

Call for Papers DRAI 2025
Data Readiness for AI Workshop
(in conjunction with ICPP 2025)

https://urldefense.us/v3/__https://sites.google.com/lbl.gov/drai__;!!G_uCfscf7eWS!cmJD5Ib6xPUij_gF4acYzwHy5P5h73M2kqbk9wQf_uX4KyDgMvwjr-0EmYfArKqaXjt_QXSD-YkWaLF11MxSKQ$ 

September 8 – San Diego, CA, USA

Submission Deadline: June 30, 2025 (extended)

Data fuels Artificial Intelligence (AI) applications. Hence, collecting and
managing the lifecycle of high-quality data that has been evaluated to be
ready for AI is critical to creating new large AI models that assist in
enabling scientific breakthroughs across domains. However, there are
several challenges in achieving AI readiness for research data. This
workshop aims to enable discussions on novel and efficient methods for
collecting and preparing data for AI across multiple scientific domains,
metrics to quantify data readiness, frameworks for improving data
readiness, assessments of the impact of data on AI model performance, and
existing challenges and caveats in managing and transforming historical and
new data from various science domains into AI-ready data. The workshop
seeks to bring together researchers from academia, industry, and national
laboratories to share insights, foster collaboration, and push the existing
boundaries between data and AI in scientific discoveries.

Topics of Interest (including but not limited to):

- Efficient Data Quality Improvement (Semi-/Automatic Methods)
- Quantifiable Metrics for AI Data Readiness
- Frameworks and Tools for Assessing & Automating Data Readiness
- Frameworks for Data Readiness Improvement
- Distributed and Parallel Algorithms for AI Data Processing (HPC
Intersection)
- Scalability of AI Readiness Solutions
- Creation and usage of AI-ready benchmark datasets
- Novel Approaches for Handling Massive Datasets
- Real-World Use Case Assessments of Data Readiness Impact on AI Performance
- Data Readiness in Integrated Scientific Pipelines: Use Cases, Challenges,
Needs
- Role of FAIR principles in AI readiness of data
- Ethical and Comprehensive Data Collection
- Collection of balanced data and the characterization of biases
- AI Algorithms for Non-AI-Ready Scientific Data
- Social Impacts of Data in AI Applications
- Data Governance: Metrics, Methods, and Use Cases (Security, Privacy,
Management)
- The importance of provenance and contextual information, e.g., as
captured in datasheets
- Transparency and ethics related to AI readiness
- AI readiness of multi-modal scientific data and streaming data

All submissions will receive at least three reviews. We strongly encourage
authors to share code, data, and artifacts alongside their papers.

Important Dates
------------------------------------------------------------------------------
Paper Submission Deadline: June 30, 2025
Notification of Acceptance: July 10, 2025
Camera-Ready Deadline: July 15, 2025
Workshop Date: September 8, 2025

Submission Guidelines
------------------------------------------------------------------------------

DRAI is accepting submissions for regular and short papers. Regular papers
should be 6 (six) pages (including references), and short papers should be
2 (two) pages (including references), both formatted according to the ICPP
2025 workshop proceedings style (ACM Sigconf format). Detailed submission
instructions are available at https://urldefense.us/v3/__https://sites.google.com/lbl.gov/drai__;!!G_uCfscf7eWS!cmJD5Ib6xPUij_gF4acYzwHy5P5h73M2kqbk9wQf_uX4KyDgMvwjr-0EmYfArKqaXjt_QXSD-YkWaLF11MxSKQ$ .
Submissions, reviews, and communications with authors will be handled
through Linklings. To submit, please access
https://urldefense.us/v3/__https://ssl.linklings.net/conferences/icpp__;!!G_uCfscf7eWS!cmJD5Ib6xPUij_gF4acYzwHy5P5h73M2kqbk9wQf_uX4KyDgMvwjr-0EmYfArKqaXjt_QXSD-YkWaLHr6oNX5g$  and select the “DRAI - Data
Readiness for AI Workshop”.

Organizers
------------------------------------------------------------------------------

Jean Luca Bez, Lawrence Berkeley National Laboratory (LBNL), USA
Suren Byna, The Ohio State University (OSU), USA

For inquiries, contact: jlbez at lbl.gov, byna.1 at osu.edu