Our mission is to spread useful AI effectively around the world.
About the team
The Google Cloud AI research team tackles underexplored, real-world challenges for Google Cloud customers. We work on a range of inspiring problems based on Google Cloud customer needs, identifying research topics that maximize both scientific and real-world impact. As such, we collaborate closely with product teams to put our research results in the hands of our customers, and publish the findings in top ML venues.
Innovations coming out of the Google Cloud AI research team help explain the behavior of sophisticated machine learning models and improve current capabilities by enabling more efficient use of data. Additionally, we proactively identify market needs, and work with customers to identify specific use cases where innovation is needed (e.g. recommendation systems, document understanding, infectious disease modeling).
Research areas
Team focus summaries
Highlighted projects
A novel approach to Covid-19 forecasting that integrates machine learning into compartmental disease modeling, and provides explainable insights for disease dynamics.
A new deep learning method for tabular data that improves over other DNN and ensemble decision tree models on many datasets and provides interpretable insights.
A framework that enables quantitatively compute the importance of each training sample for the model, which yields better quality estimates than the alternatives with significant time savings.
A DVRL based approach that yields significant improvements for domain adaptation in terms of target accuracy by jointly optimizing the data valuator and predictor.
A framework that locally distills a block box model into an interpretable model of our choice (e.g. shallow decision tree or linear regression) without sacrificing performance.
A new deep learning model for time-series that beats other algorithms by a large margin and provides useful explanations in various forms.
An easy-to-integreate training framework that improved confidence scores (calibration) for prediction without significant increase of the training and inference complexity.
Featured publications
AAAI Conference on Artificial Intelligence (AAAI) 2021 (2021)