The next frontiers of scientific discovery will be pioneered by researchers who have the inspiration and ability to transcend the traditional boundaries of science. We need new interdisciplinary thinkers, people with the capacity to cut across multiple fields to tackle the types of challenges that will define the next century — from confronting climate change, to mitigating global epidemics, to delivering clean food and clean water to people everywhere.
This should be a golden age for scientific discovery. Technology — such as optimization techniques and learning — makes possible a level of ambition, scale, and efficiency that were unimaginable 20 years ago.
But traditional funding is increasingly tight, under threat, and cautious. In this environment, it is difficult to broach new subjects, follow new ideas, or use new approaches that have not already been successful. Resources go to an ever-narrowing set of institutions and people. It is hard for smart people from diverse origins to break into new fields. Researchers easily become risk-averse and shy away from trying ideas/solutions from other disciplines or collaborating and sharing credit.
It may well be rational to invest in science with an immediately publishable result, or to fund only those people with track records, or to reward deep specialization. But taken together, such a model carries a heavy cost. Our work, and the work we hope to encourage in others, promotes lateral thinking. We invest risk capital to fund uncertain but potentially rewarding basic research. We work to ensure that we are training researchers to lead the institutions that will take on systemic challenges.
Invest in new platforms that support a new scientific research paradigm based on data science and machine learning
Encourage and test truly risky and important new ideas through nascent research projects
Grow future leaders in the sciences through broadening, interdisciplinary education, and immersive experiences
Schmidt Science Fellows
Israeli Women’s Postdoctoral Award
AI-Powered Science Accelerator
Data Storage for Research