Academic research and open-source software development are at the forefront of machine learning (ML) technology development. Since 2017, the AWS Machine Learning Research Awards (MLRA) has been aiming to advance machine learning by funding innovative research, training students, and providing researchers with access to the latest technology. MLRA has supported over 100 cutting-edge ML projects, with topics such as ML algorithms, computer vision, natural language processing, medical research, neuroscience, social science, physics, and robotics. Many of the MLRA-backed projects have received media coverage, for example, Researchers are Using Machine Learning to Screen for Autism in Children, The Robotic Future: Where Bots Operate Together and Learn from Each Other, Autonomous Vehicles: The Answer to Our Growing Traffic Woes, Amazon Gives AI to Harvard Hospital in Tech’s Latest Health Push, and Facebook’s Fight to Prevent Deepfake Dystopia Gets a Powerful Partner in Amazon Web Services.
AWS is pleased to announce that MLRA is now calling for proposals for the Q4 2019 cycle, and welcomes faculty members at accredited (Ph.D. granting) academic institutions and researchers at non-profit organizations to apply. The following types of projects are eligible for MLRA funding:
- Development of open-source tools and research that benefit the ML community at large.
- Impactful research that uses any of the following AWS ML solutions: Amazon SageMaker, Amazon SageMaker Ground Truth, Amazon SageMaker Neo, Apache MXNet on AWS, and AWS AI Services.
MLRA may provide unrestricted cash funds, AWS Promotional Credit, and training resources, including tutorials on how to run ML on AWS and hands-on sessions with Amazon scientists and engineers.
The average awarded amount is no more than $70,000 cash and $100,000 AWS Promotional Credits for individual projects. The actual amount awarded depends on the nature of the project. An internal advisory board at AWS reviews the proposals and makes funding decisions based on potential impact to the ML community, quality of the scientific content, and extent of usage of AWS AI/ML Services.
The submission deadline is at 11:59 PM (PST), December 8, 2019, and decision letters are sent out approximately three months after the submission deadline.
About the Author
An Luo, PhD, is a Senior Technical Program Manager at AWS. An spent many years applying machine learning to biomedical research. Now, she focuses on enabling and accelerating machine learning research leveraging AWS AI/ML technologies.
from AWS Machine Learning Blog