By Jeremy Nixon
Orchestrated by Seenia Hong
Judged by Julia Peng
AlphaFold's worthy recognition - the Nobel Prize in Chemistry - marks the intersectional progress of AI and Biology as one of the most fertile and victorious early research directions in an indelible transition to artificial intelligence mediated science.
It is plausible that a pragmatic agenda for saving lives, including AI powered biological research, will gain proper prominence as the highest hope of humanity. We gather to build in that spirit.
Opening Talks:
- Andrew White (Cofounder @ Future House)
- Andrew Payne (CEO @ E11)
- Atray Dixit (prev VP Tech @ Gordian, prev Cofounder & CEO @ Coral Genomics)
- Amit Deshwar (Scientific Advisor @ Aperture Therapeutics; building in stealth)
- Alessandro Migliara (Research scientist @ UCSF)
- Jacob Kimmel (Cofounder & Head of Research @ NewLimit, prev PI @ Calico Life Sciences)
- Jacob Rinaldi (Co-founder of Noetik)
- Jeremy Nixon (Chief Executive Officer @ Omniscience)
- Sophia Lugo (Cofounder & CEO @ Radar Therapeutics)
- Natalie Ma (Cofounder & Head of Business Dev @ Deep Origin)
Presentations Delivered by:
- Warspite: Elliot Roth, Casey Detrio, Sean Raspet, Zoe Isabel Senon
- Warspite PaperGen: Jeremy Nixon
- Stochastic Search Is All You Need: Justin Jung
- Paper2Graph: Jacob Cole & Calvin Xu (Ideaflow.ai, Stanford)
- NeuroForge: Brian Lynch, Manu Ponnapati, Alessandro Migliara, Grace Reed
- Finetune on Protein Language models made easy!: James Hennessy
- Can Gemini Understand A Lab?: Eric Yu
- WebPortal for XR for better protocols: Gary Yao, Matt Mo
- Personalized Deep Molecular Models ("ML for AML"): Karen Sachs, Eric Mockler
Presentation Videos:
1st Place:
WebPortal: Augmented Reality Biolab Research
Gary Yao and Matt Mo
Scientists can now show the exact process and protocols they use for experiements via Vision Pro or other XR devices.
2nd Place:
NeuroForge: Expressing Opsins in Specific Neuronal Cell Types
Brian Lynch, Manu Ponnapati, Alessandro Migliara and Grace Reed
Protien design with cellular context: Train a model to be able to predict differential expression for different cell types within a tissue.
3rd Place:
Stochastic Search Is All You Need: Adding guidance to discrete diffusion with non-differentiable value function
Justin Jung
Improve controllability in protien generation through a guidance function.