Artificial Intelligence

• Apply, adapt, and in some cases create multi-modal foundation models such as large language models (LLMs), diffusion models, and encoder architectures to answer biological domain-specific questions.
• Address real-world biological modeling challenges such as data sparsity, class imbalance, noise, experimental bias, and heterogeneity of effects.
• Thoughtful model evaluation that incorporates appropriate benchmarks, statistical tests, and problem understanding to support technical and business decisions.
• Work in close collaboration with partners across the organization including wet-lab scientists, Research IT, and other computational scientists to broaden the impact of AI developments.
• Maintain and share up-to-date knowledge of modern advances in the field, including presenting work at public conferences.

Scientist, Predictive Biology, AI

Head of AI – Services & Care

Research Project Planning Manager, AI Planning Lead

Applied AI Inference Engineer

Staff VP, AI Technology Governance & Enablement

VP, Marketing – Systems & AI