We aim to transform scientific research itself. Many scientific endeavors can benefit from large scale experimentation, data gathering, and machine learning (including deep learning). We aim to accelerate scientific research by applying Google’s computational power and techniques in areas such as drug discovery, biological pathway modeling, microscopy, medical diagnostics, material science, and agriculture. We collaborate closely with world-class research partners to help solve important problems with large scientific or humanitarian benefit.
Recent publications
Privacy-first health research with federated learning
medrxiv, vol. https://www.medrxiv.org/content/10.1101/2020.12.22.20245407v1.full (2021)
A multi-objective Markov Chain Monte Carlo cellular automata model: Simulating multi-density urban expansion in NYC
Computers, Environment and Urban Systems, vol. 87 (2021)
Learning to Approximate Density Functionals
Accounts of Chemical Research (2021)
Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance
Frontiers in Neuroscience, vol. 14 (2020), pp. 575081
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