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Andrea Jacobs

Senior Director, Artificial Intelligence

Bio

Andrea Jacobs leads the AI team at CAS that built CAS Newton — the agentic AI platform redefining how scientists discover, navigate, and apply the world's scientific knowledge. As Director of Artificial Intelligence, she oversees a team of data scientists, engineers, and domain experts whose expertise spans chemistry, materials science, life sciences, and physics, translating deep scientific expertise into AI systems that accelerate R&D across industry and academia. Her perspective is uniquely broad: over 16 years at CAS she has worked across data infrastructure, content licensing, product strategy, and enterprise partnerships, giving her a grounded view of both the science and the business of research. She holds a B.S. in Chemistry and Computer Science from Wellesley College and an MBA from The Ohio State University.

Your Next Big Scientific Discovery May Already be Published

Some discoveries are already latent in the published record, waiting for someone to read two literatures together. A look at why structured search and large language models each miss those connections, and what changes when AI reasons over verified scientific relationships rather than prose about them.
|Article

2026 Trends Authors Mid-Year Review: Science Moved Faster Than Anyone Expected

Five experts revisit their 2026 forecasts to ask: which predictions held up, which fell short, and what caught them by surprise?
|Video

Webinar Review: AI for patent searching — From prior art to future frontiers

Patent search looks like a retrieval problem, which is why general-purpose AI struggles with it. In a recent webinar, patent searchers and AI engineers explained where models fail when reading patent text, and what must sit underneath AI before its answers can be trusted.
|Webinar
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Predictive chemical deformulation flips the formulation challenge

Deformulation is the process of reverse-engineering the composition of known formulations. This publication shows the value of consistent and highly structured representations in predictive models.
|Journal Publication