International Workshop on Big Data & AI Tools, Models, and Use Cases for Innovative Scientific Discovery IEEE BTSD on December 08, 2025 in Macau, China

The 6th International Workshop on Big Data & AI Tools, Models, and Use Cases for Innovative Scientific Discovery (BTSD) 2025

Workshop Date/Time: TBD (Virtual Workshop)

Conference Date: 2025 IEEE International Conference on Big Data 8-11 December, 2025 Macau SAR, China

CALL FOR PAPERS

Program Chairs

Sangkeun (Matt) Lee

Hillary K. Fishler

Thomaz Carvalhaes

Minsu Kim

Introduction to Workshop

Big data, machine learning, artificial intelligence, and data science technologies have driven breakthroughs across diverse fields by enabling innovative ways to integrate, reuse, and analyze vast datasets. These successes have inspired scientists in physics, chemistry, materials science, and medicine to explore how these tools can advance their research. However, challenges remain. Many existing software tools and systems were not designed for scientific research or the specific needs of scientists. Scientists without programming or computer science expertise may struggle to use these tools effectively, while computer scientists

may lack the domain knowledge needed to address field-specific problems. This workshop aims to bridge the gap between domain scientists and computer/data scientists. It will foster collaboration by exploring tools, systems, and methodologies to enhance scientific discovery. Participants will share success stories, discuss lessons learned, and address challenges to promote effective cross-disciplinary partnerships.

The workshop will focus on the following questions:

• How do big data, machine learning, data science, and AI tools specifically designed for scientific research differ from traditional analytical tools?

• What challenges do scientists face in terms of capturing, representing, maintaining, integrating, validating, and extrapolating data for scientific discovery?

• What are the unique needs and hurdles domain scientists encounter when incorporating big data tools into their research?

• How can computer scientists and domain scientists collaboratively identify and define research problems more effectively?

• What obstacles hinder the application of big data in scientific discovery, and how do these challenges vary across different scientific domains?

• How can big data technologies be leveraged to improve the accuracy and reliability of scientific experiments and simulations?

• What influence does big data exert on the scientific method, and how can we ensure its utilization promotes scientific integrity and reproducibility?

Research Topics Included in the Workshop:

- Big data tools, systems, and methods are related to, but not limited to

- Scientific data processing (integration, standardization, sampling, etc.)

- Artificial intelligence and machine learning

- Text and graph mining

- Database management, query processing, and query optimization

- Parallel computation and high-performance computing

- Visualization and user interface/HCI

- Parallelization, performance, and scalability of data tools

- High-performance computing

- Uncertainty quantification

- Combinational usage of simulation, experiment, machine learning models, and data

- Data fusion which facilitate innovation and discovery in scientific domains such as:

 - Physics

 - Chemistry

 - Material science

 - Power Systems and Grid Resilience

 - Mechanical engineering

 - Nuclear engineering

 - National security

 - Biomedical science, and more.

Use cases, success stories, ongoing research with interesting questions, and lessons learned in scientific discovery using big data tools, systems, and methods are highly encouraged to submit.

Tutorial papers that demonstrate the application of useful tools for processing big data in scientific research, which can be shared with the research community. We seek submissions that highlight the practicality of these tools, clearly showing their utility. We encourage submissions that describe innovative tools designed to accelerate scientific discovery through big data.