Daphne Koller
TL;DR Daphne Koller is a pioneering computer scientist known for her contributions to probabilistic modeling, machine learning, and the development of innovative platforms that bridge AI and the life sciences.
Daphne Koller by Sora
Daphne Koller is one of the most influential researchers in artificial intelligence, celebrated for her work in probabilistic reasoning, computational biology, and translating academic research into real-world impact. Her career spans groundbreaking academic contributions, significant advances in online education, and innovative efforts at the intersection of AI and drug discovery.
Daphne Koller is a professor of computer science at Stanford University, where she has conducted foundational research on probabilistic graphical models and machine learning. Her academic work helped establish rigorous methods for reasoning under uncertainty, shaping how modern AI systems infer complex relationships from data.
Beyond academia, she played a transformative role in reshaping global education as the co-founder of Coursera, one of the first platforms to bring high-quality university courses to learners worldwide. Her leadership helped spark the international online learning movement and democratize access to advanced education.
She later founded Insitro, a company that combines machine learning, automation, and large-scale biological data to accelerate drug discovery. At Insitro, she leads multidisciplinary teams integrating AI with laboratory science to identify new therapeutic pathways and improve the efficiency of biomedical research.
Throughout her career, Daphne Koller has pushed the boundaries of what AI can achieve when paired with rigorous science and real-world application.
Pioneering research in probabilistic graphical models and machine learning
Professor of computer science at Stanford University, shaping generations of AI researchers
Co-founder of Coursera, a global leader in open online education
Founder and CEO of Insitro, advancing AI-driven drug discovery
Influential contributions to computational biology and data-driven science
Key figure in bridging academia, industry, and applied machine learning
