ICBD24 Workshop
Big Data and AI have significantly advanced the field of Healthcare in recent years. As data collection, storage, and communication technology become increasingly ubiquitous and sophisticated, research on Big Data and AI has become essential for better understanding and analyzing these vast amounts of complex data. To make rapid progress in this interdisciplinary field, this workshop aims to bring together researchers from machine learning, data mining, statistics, AI, and healthcare. We welcome submissions of papers related to methodology, design, techniques, and new directions for Big Data and AI in Healthcare.
Workshop Highlights
The workshop will be held on December 15, 2024 in Washington, DC, USA, as part of the 2024 IEEE International Conference on Big Data (IEEE Big Data 2024). We invite submissions presenting original research that has not been published or is not currently under review for another workshop, conference, or journal. Papers should be submitted electronically.
How to submit
We encourage submissions from a variety of fields related to Big Data and AI for Healthcare. By submitting your work, you express your intent to have at least one author attend the workshop and share your research with the community. We ask that the presenter complete their registration before the author registration deadline. Accepted papers will be featured in the workshop proceedings, either as regular papers or short papers.
For more details on submitting your work, including topics of interest and submission guidelines, please visit our Call for Papers page.
Confirmed Speakers
Dr. Aidong Zhang's research focuses on developing machine learning approaches to interpretable and fair learning, concept-based learning, federated learning, and generative AI. She also works on large language models for hypothesis generations for scientific discovery. Dr. Zhang is the Thomas M. Linville Professor of Computer Science, with a joint appointment in the Department of Biomedical Engineering and the School of Data Science at the University of Virginia. Her research interests include machine learning, data mining, bioinformatics, and health informatics. She received her Ph.D. in Computer Science from Purdue University.