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
Format of Workshop
This is a full-day workshop.
It will be structured into sessions aligned with distinct research themes. Each session will open with a featured invited talk, with the rest focusing on presentations by authors of accepted papers. These will vary in length depending on the submission type and reviewer feedback. A final session will contain a panel discussion by senior and up-and-coming researchers, focusing on next steps for the community.
Attendance
Invited speakers and other attendees will fall into three groups:
Foundational AI researchers
Biological researchers that have started to utilize FMs
Biological researchers with a track record in ML but not FMs
Based on our first successful offering at AAAI 2024, which focused on LLMs, we expect to attract at least 75 attendees. We do not expect to exceed 100 attendees.
Submission Requirements
To reflect the disciplinary diversity, we will encourage submissions of varying length:
1-page position papers
4-page papers on breaking results, datasets, benchmarks
6-8-page papers on more detailed investigations
Submission Site Information
Authors will submit at: https://easychair.org/my/conference?conf=fms4bio25
We have set up llms4science@gmail.com for author submission enquiries.
Accepted papers will be published at https://github.com/LLMs4Science-Community.
Workshop Organizers
Amarda Shehu, George Mason University, amarda@gmu.edu
Yana Bromberg, Emory University, yana@bromberglab.org
Liang Zhao, Emory University, liang.zhao@emory.edu
Workshop URL
https://llms4science-community.github.io