Podcast EP296: How Agentic and Autonomous Systems Make Scientists More Productive with SanboxAQ’s Tiffany Callahan
Dan is joined by Dr. Tiffany Callahan from SandboxAQ. As one of the early movers in the evolving sciences of computational biology, machine learning and artificial intelligence, Tiffany serves as the technical lead for agentic and autonomous systems at SandboxAQ. She has authored over 50 peer-reviewed publications, launched several high-impact open-source projects and holds multiple patents.
Dan explores the foundation of the agentic and autonomous systems SandboxAQ is developing with Tiffany. She describes the impact of large quantitative models, or LQMs, particularly in drug discovery and material science research. Unlike LLMs that are trained on broad-based Internet data for text reasoning, LQMs are trained on first principles of physics, chemistry and engineering, This creates AI that can reason about the physical world. SanboxAQ aims to deploy this technology as an adjunct to existing research experts by simulating and predicting physical outcomes on a massive scale. This provides scientists with tools that are both grounded in physical science and generative, facilitating more targeted and efficient research,
You can learn more about this unique company and the impact it aims to have on advanced research here.
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