Google Accelerated Science Team

About our work

Our mission is to increase the rate of scientific discovery with Google technologies, including machine learning, iterative prediction/experimentation in large combinatorial spaces, and large scale analysis and computation. We believe these will enable more effective high throughput research in many domains.

Using Google's unique expertise, technology and scale, we collaborate with world-class institutions on challenges with large scientific and humanitarian benefit, working closely with leading scientists who have deep domain expertise and proven experimental infrastructure.

Here's a small sample of our partners.

In Cell Screening:

In Cell State:

In Drug Discovery:

In Energy:

In Materials Science:

In Quantum Chemistry:

Some of Our Research Areas

Our projects span many fields of science, including biology, chemistry, materials science, and physics. We actively investigate new scientific problems in areas we haven't worked in yet, and maintain a flexible scope on a wide range of potential projects.

Automatic insight from biological images

We use machine learning to scalably interpret biological images. Our approaches offer many benefits including non-invasively imputing deep cell measures on transmission microscopy images (without stains), and discovering new phenotypes and common mechanism of actions across large cell screens.

Predicting chemical interactions

We use several machine learning approaches to represent molecules and predict their interactions. We’ve tackled a range of problems using in silico screening of molecules for affinity to specific targets, a useful step in discovering new drugs.

Predicting results of complex physical simulations

While there exist many computational approaches to accurately simulating molecular or material properties, often these simulations are too expensive to discover molecules or do materials designs at scale. We are using machine learning to predict the outcome of expensive simulations, allowing us to short circuit computationally difficult problems.

Search large combinatorial space with high-throughput physical experiments

We use design of experiment techniques and machine learning to explore large combinatorial spaces with feedback from physical experiments. These problems range from designing a robotic protocol for differentiating stem cells, to determining the right mix of atoms to create a new functional material, to optimizing settings for a plasma fusion machine.

Some of Our Publications

Some of Our Team

 
Senior Software Engineer
 
Director, Engineering
 
Director, Product Management
 
Director, Engineering
 
Principal Engineer