Anna Marie Wagner
Anna Marie Wagner's career has spanned investing and operating roles in both hypergrowth and mature companies. She brings a unique breadth of experience, having served as an investor as well as a hands-on executive leading multiple strategic functions across industries, particularly technology and life sciences. She is currently the co-founder and CEO of Transfyr Bio, which is focused on improving scientific interpretability, robustness, and transferability and advises companies across both the public and private sectors. Previously, Anna Marie was on the executive team at Ginkgo Bioworks, where over the years she built and led numerous functions including finance, corporate development, and AI. Anna Marie led Ginkgo's public offering in 2021, raising $1.6 billion in what is still the largest-ever go-public raise in biotech, and Ginkgo's collaboration with Google. She is also an Executive Fellow at Harvard Business School, where she co-teaches an elective course on failure (and loves the irony!).
Transfyr
Bigger Data vs. Better Models – Finding the Right Scale for Bio-AI
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In AI development, there’s a trade-off between scale and sophistication. This session asks whether biological AI should follow the “bigger is better” mantra or focus on smarter, domain-specific architectures. One camp argues that a simple model fed with colossal datasets will outperform a clever model with limited data – echoing the view that more data beats complex algorithms. Others point out that biology’s complexity (from multi-step pathways to 3D genome organization) demands AI with built-in knowledge or special architectures to learn effectively from smaller, high-quality datasets. Through case studies in drug discovery and genomics, we will discuss if success lies in scaling up simple neural networks on big data or in engineering biologically informed AI models that excel with less data. What are the ROI trade-offs for researchers and investors in each approach?
