Topics

We encourage participation on a range of topics exploring the synergy between scientific knowledge and ML, including (but not limited to): 

• Use of scientific knowledge as loss functions or hard constraints in the training of ML models for supervised, unsupervised, and semi-supervised applications.
  
  

• Design of deep learning architectures that are grounded in scientific theories and generate explainable and physically meaningful feature representations.
  
  

• Use of simulated data generated by science-based models along with observations in ML frameworks.
  
  

•  Techniques to augment imperfections or infer parameters in science-based models using ML.
  
  

• Use of scientific knowledge in the design, pretraining, or finetuning of Foundation models in science.
  
  

• LLMs for accelerating scientific discovery.

Submission Requirements

We are accepting two types of submissions, namely short submissions (maximum 2 pages excluding references) as extended abstracts, proposals, full paper submissions (maximum 6 pages excluding references), in a variety of tracks such as:

1. Lecture-style Tutorials Track: We welcome short proposals on lecture-style tutorials that survey or provide new perspectives of a research area in KGML, with a discussion of prior literature in the area and opportunities for future research. Proposals should include an overview of the research topics that will be covered, target audience and required background knowledge, intended length of the tutorial (ranging from 20 minutes to 1 hour) and tentative format, supporting references, and prior qualifications of the team in the context of delivering related tutorials.

2. Hands-on Tutorials Track: The goal of this track is to impart practical understanding of state-of-the-art research methodologies in KGML by working through examples and demonstrating the application of KGML to real-world use cases. This includes demonstrations of KGML algorithms, benchmark datasets, tools, code-bases, or coding platforms in an interactive hands-on format that is easy to follow for a broad audience. Proposals submitted to this track should include an overview of the tutorial topics, target audience, required background knowledge and software requirements, intended length of the tutorial (ranging from 45 minutes to 1.5 hours) and tentative format, supporting references, and prior qualifications of the team in the context of delivering related tutorials.

3. Early Career Lightning Talks Track: We want to promote next-generation leaders in the field of KGML including graduate students, 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.

4. Poster Track: We welcome extended abstracts of posters showcasing new or existing problems or use-cases of KGML in scientific disciplines, novel methods, or new datasets and evaluation benchmarks. 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.

5.  Regular Paper Track. Early stage or detailed experimental investigations of topics related to scientific machine learning, knowledge-guided machine learning are also encouraged. 

The title of the submission should clearly state which one of the five 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.