Five Women Shaping the AI–Life Science Stack

This article is brought to you by our writer Anastasiia Rohozianska; also, check out our regular BiopharmaTrend.com contributor, Dr. Louise von Stechow, who writes on AI, biotech strategy, rare disease therapeutics, and emerging modalities.

Each year, the day has a different theme. This year it’s “Synergizing AI, Social Science, STEM and Finance: Building Inclusive Futures for Women and Girls,” which makes it natural to first ask where women generally stand in science, AI and finance today before looking at those who are now shaping these systems.

The numbers show that progress has been slow: women still account for roughly one-third of researchers worldwide and about 35% of STEM graduates, a proportion UNESCO data indicate has barely shifted in a decade. Inside the AI pillar that is rapidly reshaping drug discovery, healthcare and finance, women are estimated to hold around a quarter of AI jobs and less than 15% of senior roles, while a global synthesis of usage studies finds women about 20% less likely than men to engage with generative AI tools in the first place. At the same time, a UN analyses suggest a higher share of women’s jobs than men’s are exposed to AI-enabled automation, particularly in clerical and administrative roles, making it more likely that women experience AI as something that reshapes or displaces their work rather than an arena where they set agendas and build systems.

At BiopharmaTrend, we cover the intersection of advanced biology, digital technologies, and emerging therapeutics—so for this occasion, we focus on STEM through the lens of life sciences.

Today, genomics, cell signalling, neurotrophic pathways and infectious-disease therapeutics are standard components of drug discovery pipelines, but much of this infrastructure traces back to scientists whose careers were shaped by structural barriers and delayed recognition, which is why an international observance focused on women and girls in science is not just symbolic context for an AI-era discussion, but part of the story: modern biomedicine is built on work that women often completed without full recognition.

• Rosalind Franklin’s X-ray crystallography on DNA and viruses was central to solving the double helix and later structural virology, but her role was widely recognized only decades later. 
• Barbara McClintock’s maize genetics revealed transposable elements and genes as mobile (“jumping genes”), now fundamental to genomics and epigenetics, yet her Nobel Prize came more than 30 years after her first reports. 
• Working under fascist racial laws that barred Jewish scientists from universities, Rita Levi-Montalcini identified nerve growth factor, the first growth factor and a basis of modern cell and neurobiology. 
• Tu Youyou’s isolation of artemisinin created a new class of antimalarial drugs that have saved millions of lives and remain standard malaria treatment, while her contribution stayed largely unknown outside China for many years.

Below, we turn to women who are defining research, product, and investment priorities across AI and life sciences, rather than appearing only as those whose jobs, data, or care are shaped by these systems.

Daphne Koller — Founder & CEO at insitro

Daphne Koller, PhD, is a computer scientist and entrepreneur who moved from foundational work in probabilistic graphical models and Bayesian machine learning at Stanford into building an AI-native drug discovery company as founder and CEO of insitro. Koller’s academic career spans key contributions at the interface of AI, computer vision, and computational biology, recognized with a MacArthur Fellowship, the ACM Prize in Computing, and election to the US National Academy of Engineering and National Academy of Sciences. She also co‑founded Coursera and later co‑founded Engageli; today she remains engaged in digital learning while focusing her primary efforts on applied biology and therapeutics at insitro.

At insitro Dr. Koller oversees a platform that couples high‑throughput human cellular systems with machine learning models optimized for target discovery and molecule design. The platform builds iPSC‑derived disease models using human genetics and functional genomics, while complementary systems such as the platform combine pooled CRISPR perturbations, high‑content imaging and self‑supervised deep learning; together these platforms generate multimodal, multi‑omics datasets that feed cell‑level ML models.