We build ultra-long-context genomic foundation models to predict disease risk, explain heritability, and design actionable edits from whole-genome and multi-omics data.
Ultra-long context architectures pretrained on whole genomes to capture regulatory logic across megabases.
Variant effect, expression, and disease-risk prediction grounded in multi-omics, calibrated to clinical use.
From hypothesis to edit — close the loop between in-silico prediction and wet-lab validation faster.
Genomic Intelligence models are now callable from a growing set of agent platforms, and from any MCP client. One integration standard, a REST API and a hosted MCP server, is what made each of those a short piece of configuration rather than a project.
Read →[G×I] expression model identifies more than 200 variants with strong predicted effects on GLP1R expression.
Read →The Genomic Intelligence MCP server is now published in the official Model Context Protocol registry, so registry-aware clients can discover it by name with no configuration written by hand.
Read →Product vision and incremental experimentation.
Wet-lab validation and genomic model science.
Ultra-long context models and memory architectures.
Data platforms, infrastructure, and scale.
AI-for-health strategy and ecosystem leadership (Microsoft CSO; founder of Stanford AI100; former AAAI President).
AI infrastructure and large-scale ML leadership across scientific and health applications (VP & Field CTO, ML at Cerebras Systems; former Software and AI research leader at Hewlett Packard Labs).
Member of AWS Frontier AI
Cloud infrastructure and enterprise go-to-market.
Healthcare technology collaboration
Clinical data infrastructure and hospital distribution.
Biological design collaboration
Model-driven discovery and experimental validation.
Scientific-agent integration
Genomic models embedded into AI-powered research workflows.
Investors, partners, and researchers — let's talk about programming biology together.