AAAI 2025FOUNDATION MODELS FOR BIOLOGICAL DISCOVERIES

ABOUT

Foundation models (FMs) have transformed natural language understanding and computer vision. In particular, research on LLMs and multi-modal LLMs in these two domains is progressing rapidly, and this progress is starting to permeate a broad range of scientific disciplines. In this second offering of our workshop, our focus is on FMs for advancing biological discoveries. Current efforts have revealed that indeed FMs are advancing our ability to conduct biological research in silico, formulate interesting hypotheses and even design novel molecules, but biology remains complex and is ultimately a multi-systems discipline. Biology occurs when molecules come together, governed by an underlying physics advancing processes that occur at disparate spatio-temporal scales, only probed in the wet laboratories at different conditions, at different granularities, at different levels of fidelity, and incompletely. This workshop poses and advances the following question: How can we advance FMs to transform biological research? This workshop brings together an interdisciplinary community of researchers at various levels of their career to nucleate a community that advances this question.

TOPICS

In addition to the following research themes, we encourage novel contributions from researchers that bring different perspectives on the core focus of the workshop:

  • Learning from Incomplete Data of Different Modalities
  • Grounding Foundation Models in Knowledge Beyond the Data
  • Reconciling Disparate Spatio-temporal scale and Varying Fidelity in Multimodal Data
  • Beyond Prediction: Answering the How and the Why
  • Quantifying Confidence of Predictions with Foundation Models

PROGRAM

SUBMISSION

To reflect the disciplinary diversity, we will encourage submissions of varying length:

  • 1-page position papers
  • 4-page papers on breaking results, datasets, benchmark
  • 6-8-page papers on more detailed investigations
  • 10-page surveys on topics aligned with the theme of the workshop

Each manuscript should be submitted in a single PDF file, including all content, figures, tables, and references, following the format of AAAI conference papers. Paper submissions need to include author information (reviews are not double-blinded).

Papers should be submitted at: https://easychair.org/my/conference?conf=fms4bio25.

Concurrent submissions to other journals and conferences are acceptable. Accepted papers will be presented as posters or short talks during the workshop and published on the workshop website at https://llms4science-community.github.io/aaai2025. We encourage authors of accepted papers to submit datasets at https://github.com/LLMs4Science-Community. Selected accepted papers will be presented as contributed talks. As a tradition, accepted workshop papers are NOT included in the ACM Digital Library. The authors maintain the copyright of their papers. Author enquiries should be directed at llms4science@gmail.com.

IMPORTANT DATES

Following are the key dates for the workshop. All deadlines are “anywhere on earth” (UTC-12).

  • Paper submission deadline: November 30, 2024
  • Notification of decision: December 9, 2024
  • Early AAAI 2025 Registration Deadline: December 19, 2024
  • Workshop Day: March 4, 2024

ATTENDANCE

For each accepted paper, at least one author must attend the conference and present their work. Authors of all accepted papers must prepare a final version for publication and a three-minute short video presentation (further details will be provided in the acceptance notification).

KEYNOTE SPEAKERS

Allison Heath

Center for Data Driven Discovery in Biomedicine

heathap@chop.edu

GENERAL CO-CHAIRS

Amarda Shehu

George Mason University

amarda@gmu.edu

Yana Bromberg

Emory University

yana@bromberglab.org

Liang Zhao

Emory University

liang.zhao@emory.edu

STUDENT CO-ORGANIZERS

Weisen Zhao

George Mason University

wzhao9@gmu.edu

Yifei Zhang

Emory University

yifei.zhang2@emory.edu

Ethan Lee

Emory University

ethan.lee@emory.edu

Samuel Blouir

George Mason University

Manpriya Dua

George Mason University

Asher Moldwin

George Mason University