Excited to present our work on multi-objective scientific discovery at #NeurIPS2025! 🎉 We present Preference-Guided Diffusion, a novel method to sample diverse designs from a diffusion model that corresponds to the Pareto Front of black-box objectives. Most (if not all) problems in scientific discovery, from drug design to material design, are multi-objective. Balancing factors like toxicity and activity or stability and synthesizability requires finding the optimal Pareto Front of trade-offs. Preference-Guided diffusion uses preference pairs from just an offline dataset to sample optimal designs from a diffusion model. W/ amazing co-authors @syrineblk, @StefanoErmon, @stefanAbauer, and @BeEngelhardt. Find out more: 🗓️ Poster #915 | Wednesday | 4:30 PM PST Pdf: openreview.net/pdf?id=moiVS9… Project: github.com/yannadani/pgd_moo

We’re thrilled to announce the workshop “Experimental Design: AI for Science” happening at Stanford University on 3rd and 4th April 2025. The workshop focuses on theory and methodology of AI-based experimental design and its application to science. edai4science.github.io/

Please play with these data! There is a lot more signal there. Thank you to @bioimagearchive.bsky.social for hosting these Incucyte image data -- this is a new thing for them, and they have been so kind in working through the details of submission (link coming soon!)! 🎉