AWS Machine Learning Blog

Announcing AWS Machine Learning Research Awards

We are excited to announce the AWS Machine Learning Research Awards, a new program that funds university departments, faculty, PhD students, and post-docs that are conducting novel research in machine learning (ML). We are working with Carnegie Mellon University, California Institute of Technology (Caltech), Harvard Medical School, The University of Washington, and the University of California, Berkeley on this program.

The scale and performance of the AWS Cloud, coupled with powerful frameworks like Apache MXNet, TensorFlow, Caffe2, Microsoft Cognitive Toolkit (CNTK), and PyTorch, allow an unprecedented opportunity to drive machine learning research forward. The goal of this program is to help researchers accelerate the development of innovative algorithms, publications, and source code across a wide variety of machine learning applications and focus areas. In addition to funding, award recipients receive computing resources, training, mentorship from Amazon scientists and engineers, and have the opportunity to attend a research seminar at the AWS headquarters in Seattle.

AWS Machine Learning Research Awards program features

Researchers benefit from the program in a number of ways:

  1. Funding – Awards are distributed at the department and project level, and are structured as one-time unrestricted gifts to academic institutions.
  2. AWS Credits – Awards include AWS credits that can be redeemed towards any service, including the EC2 P3 instance type and the Deep Learning AMI.
  3. Training – We provide universities with training resources, including tutorials on how to run machine learning on AWS and hands-on sessions with Amazon scientists and engineers.
  4. Research Seminar – Award recipients will be invited to a research seminar at AWS headquarters in Seattle where they can discuss their work and interact with Amazon scientists.
  5. Powerful ML Tools – Researchers can utilize powerful infrastructure and tools to accelerate their research. For example, the EC2 P3 instance type is optimized for machine learning and delivers tremendous gains in model training time by using the cutting-edge performance of NVIDIA Tesla V100 GPUs. To help researchers get started quickly, the Deep Learning AMI is provisioned with many popular deep learning frameworks, each of which provides an easy-to-launch tutorial to demonstrate proper installation, configuration, and model accuracy.

Early feedback from faculty researchers

Carnegie Mellon University

“The capabilities students have available to them at their fingertips today via the AWS Cloud are amazing, and it’s great that a sophisticated framework tool such as Apache MXNet is available,” said Andrew Moore, dean of the School of Computer Science at Carnegie Mellon University. “The opportunity to have the next generation of machine learning practitioners and researchers make the most from these tools is exciting, and is uniquely enabled through this funding program. We can’t wait to get started.”

California Institute of Technology

“At Caltech we’ve been investing heavily in creating opportunities for our students to interact with and learn about the latest innovations like machine learning,” said Adam Wierman, Professor and Executive Officer of Computing and Mathematical Sciences at the California Institute of Technology. “The partnership with AWS will help us continue to make these investments, which we believe could lead to many future breakthroughs.”

Harvard Medical School

“We are excited about the new AWS ML Research Awards program, and the opportunity to use AWS credits to conduct novel AI research in the field of neurological speech disorders that affect tens of millions worldwide,” said Kristina Simonyan, Director of Laryngology Research at Massachusetts Eye and Ear Infirmary, a teaching hospital of the Harvard Medical School. “Today, the diagnostic accuracy of the majority of these disorders is unreliable, and consensus between physicians is generally hard to achieve. The development of an ML-based objective diagnosis of neurological speech disorders would be groundbreaking in significantly reducing the burden of the physician’s rate of mis- and under-diagnosis, improving the patient’s quality of life, and greatly decreasing the overall costs associated with delayed treatments of these disorders. The computational capabilities of AWS Cloud are unprecedented for making this research possible.”

University of Washington

“It is an incredibly exciting time to be working at the forefront of research at UW. As we study functional networks of the brain, deep learning is an important tool for modeling the non-linear dynamics we observe,” said Emily Fox, Amazon Professor of Machine Learning at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. “We are thrilled about the new AWS ML Research Awards program, which would allow the University of Washington to significantly accelerate the state of the art in machine learning and our understanding of the brain through the combination of the AWS Cloud and Apache MXNet.”

University of California, Berkeley

“What we love most about using AWS for machine learning is the consistent growth in computational power that AWS makes available to us on a regular basis,” said Ben Recht, Associate Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley. “Every time AWS launches a new service, feature, or instance type, the grad students immediately figure out a way to use them to streamline their research projects. We are particularly excited to train our models on the super-fast P3 instances with new GPU architecture, which would allow us to iterate faster and train more complex models. We look forward to collaborating with the AWS ML Research Awards program.”

Learn more

For more information about AWS ML Research Awards, including information on how to apply, see

About the Author

Sebouh Der Kiureghian is a Sr. Product Manager at AWS Deep Learning where he focuses on enabling academic research in machine learning through cloud services, grants, and collaboration with AWS scientists. In his spare time he likes to snowboard.