BOOM 2019:: 4th IJCAI International Workshop on Biomedical infOrmatics with Optimization and Machine learning (BOOM) The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019) Macao, China, August 11, 2019 |
Conference website | https://www.ijcai-boom.org |
Submission deadline | May 5, 2019 |
Biomedical informatics as an emerging field has been fascinating talents from artificial intelligence and machine learning for its unique opportunities and challenges. Fast-growing biomedical and healthcare data have encompassed multiple scales ranging from molecules, cells, individuals, to populations and have connected various entities in healthcare systems such as providers, pharma, and payers with increasing bandwidth, depth, and resolution. These data are becoming an enabling resource to harness for scientific knowledge discovery and clinical decision making. Meanwhile, the sheer volume and complexity of the data present major barriers toward their translation into effective clinical actions. In particular, biomedical data often feature large volumes, high dimensions, imbalance between classes, heterogeneous sources, noises, incompleteness, and rich contexts, which challenges the direct and immediate success of existing machine learning and optimization methods. For instance, deep learning methods have made notable advances for biomedical informatics needs, especially in processing brain-imaging data and making neuroscience discovery, although their utilities to more types of data in more biomedical informatics use-cases still awaits further assessment and development. Therefore, there is a compelling demand for novel algorithms, including machine learning, data mining and optimization, that specifically tackle the unique challenges associated with biomedical and healthcare data and allow decision-makers and stakeholders to better interpret and exploit the data.
The BOOM workshop aims at catalyzing synergies among biomedical informatics, artificial intelligence, machine learning, and optimization. This workshop is targeting an audience of applied mathematicians, computer scientists, industrial engineers, bioinformaticians, computational biologists, clinicians and healthcare researchers who are interested in exploring the emerging and fascinating interdisciplinary topics. It is designed to foster exchange of ideas between often-disparate groups that are unaware of each other's research, and to stimulate fruitful collaborations among different disciplines. In the past, BOOM has been successfully held three times in conjunction with IJCAI (2016, 2017, 2018), featured keynote speakers from academia, federal agency, medical practice, and corporates, successfully attracted a broad audience, and published journal special issues for accepted long articles.
Submission Guidelines
The BOOM workshop will feature on one-day events. We plan to invite 3-4 keynote speakers who are leading experts from both academia and industry. We appreciate the continuing generous sponsorship from Microsoft Research, based on which we will set up the best paper and best poster awards, in order to encourage more attendances. We plan to solicit papers of two different formats :
- full papers describing original research work that have not been published before, which will be published in a special issue of a partner journal (TBA).
- short abstracts highlighting significant works that have been published or accepted recently, which will be included in the workshop online proceedings. This is intended to provide opportunities for more high-quality work, and to encourage presentation of mature research projects that are interesting to the community.
All submissions will be considered for oral and poster presentations at BOOM. The decision on presentation format will be based primarily on an assessment of breadth of interest, and the construction of balanced and topically coherent sessions, while full papers will be given some priority for oral presentations.
List of Topics
We invite submissions of technical papers and abstracts, with important new theories, methods, applications, and insights at the intersection of artificial intelligence, machine learning, optimization, and biomedical informatics. The topics of interest include, but are not limited to, the following inter-linked ones:
Category I: Machine Learning and Optimization Algorithms
- Developing and applying cutting-edge machine learning (e.g., deep learning) and optimization (e.g., large-scale optimization) techniques to tackle real-world medical and healthcare problems.
- Addressing challenges and roadblocks in biomedical informatics with reference to the data-driven machine learning, such as imbalanced dataset, weakly-structured or unstructured data, noisy and ambiguous labeling, and more.
- Designing novel, applicable numerical optimization algorithms for biomedical data, that is usually large-scale, high-dimensional, heterogeneous, and noisy.
- Re-visiting traditional machine learning topics such as clustering, classification, regression and dimension reduction, that find application values in newly-emerging biomedical informatic problems.
- Other closely-related disciplines, such as image processing, data mining, new computing technologies and paradigms (e.g., cloud computing), control theory, and system engineering.
Category II: Biomedical Informatics Applications
- Computational Biology, including the advanced interpretation of critical biological findings, using databases and cutting-edge computational infrastructure.
- Clinical Informatics, including the scenarios of using computation and data for health care, spanning medicine, dentistry, nursing, pharmacy, and allied health.
- Public Health Informatics, including the studies of patients and populations to improve the public health system and to elucidate epidemiology.
- mHealth Applications, including the use of mobile apps and wearable sensors for health management and wellness promotion.
- Cyber-Informatics Applications, including the use of social media data mining and natural language processing for clinical insight discovery and medical decision making.
Committees
Organizing committee
- Zhangyang Wang, Texas A&M University
- Yang Shen, Texas A&M University
- Shuai Huang, University of Washington
- Jiayu Zhou, Michigan State University
Invited Speakers
- Xinbo Xu, Professor of Computer Science, Toyota Technological Institute at Chicago and the University of Chicago, USA.
- Dacheng Tao, Professor of Computer Science and ARC Future Fellow, University of Sydney, Australia
(To be updated)
Contact
All questions about submissions should be emailed to: ijcai1boom@gmail.com