Scientific knowledge-guided machine learning (KGML) is an emerging field of research where scientific knowledge is deeply integrated in ML frameworks to produce solutions that are scientifically grounded, explainable, and likely to generalize on out-of-distribution samples even with limited training data. By using both scientific knowledge and data as complementary sources of introduction in the design, training, and evaluation of ML models, KGML seeks a distinct departure from black-box data-only methods and holds great potential for accelerating scientific discovery in a number of disciplines.
The goal of this bridge is to nurture the community of researchers working at the intersection of ML and scientific areas and shape the vision of the rapidly growing field of KGML. This bridge is a continuation of the KGML 2024 Bridge organized at AAAI 2024, the KGML 2024 Workshop organized at the University of Minnesota, the AAAI Fall Symposium Series organized in 2020, 2021, and 2022, and two previous NSF-funded workshops (KGML2020 and KGML2021). See the KGML book and a recent perspective article for a coverage of topics in KGML.
We are accepting short submissions (maximum 2 pages excluding references) as extended abstracts or proposals in a variety of tracks such as:
Blue Sky Ideas Track: We welcome short papers on a position or perspective of a research area in KGML. The paper can explore why this chosen research area in KGML is relevant, how well has it been explored in previous works, and what is the intended impact of this new position or perspective proposed by the authors.
Tutorials Track: The goal of this track is to impart conceptual or practical understanding of state-of-the-art research methodologies in KGML. We welcome lecture-style tutorials that provide an overview of the research topics that will be covered, or hands-on tutorials that work through examples and demonstrate the application of KGML to real-world use cases. This could include demonstrations of KGML algorithms, code-bases, or coding platforms in an interactive hands-on format that is easy to follow for a broad audience.
Poster Track: We welcome extended abstracts of posters showcasing new or existing problems or use-cases of KGML in scientific disciplines. Shorter versions of articles in submission or accepted at other venues are acceptable as long as they do not violate the dual-submission policy of the other venue.
Datasets and Benchmarks Track: We welcome submissions discussing new datasets and evaluation benchmarks on problems in scientific disciplines involving the use of KGML methods.
Early Career Lightning Talks Track: We want to promote next-generation leaders in the field of KGML including postdocs and early career investigators by giving them an opportunity to present 5-minute lightning talks on their research at our event. Submissions should include a description of the research goals and prior work of the researcher in KGML, their motivation for attending the bridge, and a short author bio.
Dissertation Forum Track: We welcome graduate students to discuss their dissertation research in topics relevant to KGML. Submissions should include a description of the dissertation research of the student, their motivation for attending the bridge, and a short author bio.
The title of the submission should clearly state which one of the four tracks is being targeted. Submissions should be formatted according to the AAAI template (two-column, camera-ready style; see Author Kit) and submitted via EasyChair. Please feel free to reach out to the organizing committee if you have any questions about the submission instructions.