Through a contribution of $5 million worth of cloud computing credits, students and researchers are given the ability to push the boundaries of data science. Projects spanning data-centric engineering, blockchain, healthcare and secure cloud computing show the versatility of the cloud.
With droughts, floods, and hurricanes in the international news, predicting natural disasters and assessing the state of the earth is more and more urgent. The 2017 NSF BIGDATA grant sponsored by Columbia University builds on Google Cloud Platform to help predict natural disasters.
By sharing costs across a large number of academic libraries, CADRE creates a cloud-based solution for making licensed big datasets available to its member institutions--with appropriate security, stewardship, and storage--at a fraction of what it would cost them to do alone.
By combining clinical data, machine learning, and the scalable infrastructure of Google Cloud Platform, Emory University’s sepsis prediction engine uses real-time analytics in an effort to provide better care for at-risk patients while also controlling medical costs.
As universities look to redefine the student experience, Nova Southeastern University is changing the way it supports students, one AI chatbot at a time. Using Microsoft Bot Framework and Azure Cognitive Services, NSU offers a bold effort to chart a new way of engaging the next generation of learners.
Using machine learning through Amazon Web Services, OsoMe unites data scientists and journalists in studying the role of media and technology in society, and curbing the spread of misinformation online and the manipulation of social media.
Facing limited virtual machine capacity, San José State University's Computer Science Department needed a way to expand resources in order to facilitate unlimited programming exercises for computer science students.
In a UC Davis Capstone course, student teams used Google Cloud Platform to produce an astonishingly diverse array of projects. They were able to study, design, implement, and evaluate an interdisciplinary design challenge using computer and computational systems.