People building what comes next.

Why RACH recognizes Judea Pearl

For foundational work in probabilistic and causal reasoning that helps researchers distinguish prediction, intervention and counterfactual questions.

Why RACH recognizes Carol Greider

For helping discover telomerase and connecting chromosome-end biology with inherited disease and targeted clinical decisions through shared experimental and medical research.

Why RACH recognizes Anne Lacaton

For helping demonstrate how existing housing can gain space, light and useful life while respecting residents, through shared design work that challenges demolition as the default.

Why RACH recognizes Barbara Liskov

For advancing methods for understandable, reusable and fault-tolerant software, teaching those methods, and supporting a more inclusive research community.

Why RACH recognizes Yann LeCun

For enabling advances in trainable computer vision, shared research resources and scientific communities, with credit to collaborators and clear limits on claims about future AI.

Why RACH recognizes Drew Weissman

For collaborative discoveries that helped make modified-mRNA vaccines practical, with shared credit for their clinical development and a clear distinction between established benefits and experimental ambitions.

Why RACH recognizes Alejandro Aravena

For collaborative work on housing that preserves access to the city and allows adaptation, and for bringing residents’ needs into architectural practice—while acknowledging costs, risks and mixed long-term outcomes.

Why RACH recognizes Vint Cerf

For collaborative development of Internet architecture and protocols, and sustained work on institutions and access that make shared communication possible.

Why RACH recognizes Geoffrey Hinton

For collaborative advances in learning representations, speech recognition and image classification, and for bringing the responsibilities of powerful AI into public debate.

Why RACH recognizes Mary-Claire King

For establishing inherited breast-cancer susceptibility and applying genetic evidence to human rights, with families, counselors, and collaborating scientists central to the story.

Why RACH recognizes Anna Heringer

For collaborative educational, care, and working spaces that develop local skills and materials, with shared credit and clear limits on unmeasured outcomes.

Why RACH recognizes Radia Perlman

For contributions to resilient, manageable computer networks and technical education, with shared credit for standards, implementations, and the people who keep infrastructure working.

Why RACH recognizes Fei-Fei Li

For co-building shared foundations for visual AI research and broader educational participation, with credit to collaborators and clear scrutiny of bias, privacy, and unproven commercial claims.

Why RACH recognizes Katalin Karikó

For helping establish the modified-mRNA science that enabled effective vaccines, with credit to collaborators and a clear distinction between demonstrated medical benefit and new experimental ambitions.

Why RACH recognizes Shigeru Ban

For expanding architectural attention to shelter, privacy, and community life after disaster, through material experimentation and collaboration with engineers, local teams, volunteers, and residents.

Why RACH recognizes Brewster Kahle

For helping build shared infrastructure that preserves digital culture and makes sources accessible to readers, researchers, and libraries—with credit to the teams and communities behind it.

Why RACH recognizes Yoshua Bengio

For shared advances in learning representations, neural language modeling, attention and generative models, and for research tools, teaching, and institution-building—not for a proven guarantee of safe AI.

Why RACH recognizes David Sinclair

For contributions to experimental aging research, cellular reprogramming, and research tools and teams—not for a proven cure for human aging or an endorsement of commercial products.

Why RACH recognizes Demis Hassabis

For helping build and lead the teams behind AlphaGo and AlphaFold—and for helping turn advances in artificial intelligence toward scientific discovery. The achievement is shared, and its future promises remain subject to evidence.