Winners of AWS Machine Learning Research Awards announced

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The AWS Machine Learning Research Awards (MLRA) provides unrestricted cash funds and AWS Promotional Credits to academics to advance the frontiers of machine learning (ML) and its applications. MLRA is pleased to announce winners for its 2019 Q2/Q3 call-for-proposal cycles:

• Mohit Bansal, University of North Carolina Chapel Hill, Auto-Adversarial Training to Make Dialogue Systems Robust to Human Errors
• Katherine E. Battle and Andre Python, University of Oxford, A Bayesian Reinforcement Learning Algorithm to Predict the Risk of Malaria in Low-endemicity Context
• Roghayeh (Leila) Barmaki, University of Delaware, Applied Machine Learning for Social Development of Children with Autism
• Joseph F. Coughlin, Massachusetts Institute of Technology, Semi-automated Eye Glance Annotation and Classification using AWS Enabled Tools
• Stefano Ermon, Stanford University, Detecting and Handling Anomalies with Robust Machine Learning Systems
• Yong Jae Lee, University of California Davis, Real-time Object Instance Segmentation
• Fei Liu, University of Central Florida, Meeting Browsing with Multiple Granularities: Automatic Summarization and Keyphrase Extraction
• Jeffrey Liu, Massachusetts Institute of Technology, Integrating the Low Altitude Disaster Imagery (LADI) Dataset into the MIT Beaver Works Curriculum
• Karen Livescu, Toyota Technological Institute at Chicago, Multilingual Acoustic-Semantic Embeddings of Spoken Language
• Scott Loarie, iNaturalist, Learning a Training Dataset for Large-scale Classification
• Michael Mahoney, University of California Berkeley, Efficient Neural Networks through Systematic Quantization
• David C. Parkes, Harvard University, Deep Learning Framework for Optimal Economic Design
• Philip Resnik, University of Maryland, Machine Learning for Mental Health in a Secure AWS Data Enclave

Congratulations to these researchers and we look forward to supporting their research!

About MLRA

MLRA aims to advance ML by funding innovative research and open-source projects, training students, and providing researchers with access to the latest technology. Since 2017, MLRA has supported over 150 research projects from more than 60 schools and research institutes in over 10 countries, on topics such as ML algorithms, computer vision, natural language processing, medical research, neuroscience, social science, physics, and robotics. 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.

For more information about MLRA, see the MLRA website or send an email to aws-ml-research-awards@amazon.com.

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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.

 

 

 

 

 

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