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2019 Q4 AWS Machine Learning Research Awards recipients announced

The AWS Machine Learning Research Awards (MLRA) is pleased to announce the 28 recipients of the 2019 Q4 call-for-proposal cycle.

By Amazon Research Awards team
July 1, 2020
3 min read
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The AWS Machine Learning Research Awards (MLRA) aims to advance machine learning (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 180 research projects from 73 schools and research institutes in 13 countries, with topics such as ML algorithms, computer vision, natural language processing, medical research, neuroscience, social science, physics, and robotics.

On February 18, 2020, we announced the winners of MLRA’s 2019 Q2/Q3 call-for-proposal cycles. We’re now pleased to announce 28 new recipients of MLRA’s 2019 Q4 call-for-proposal cycle. The MLRA recipients represent 26 universities in six countries. The funded projects aim to develop open-source tools and research that benefit the ML community at large, or create impactful research using AWS ML solutions, such as Amazon SageMaker, AWS AI Services, and Apache MXNet on AWS. The following are the 2019 Q4 award recipients:

Congratulations to all MLRA recipients! We look forward to supporting your research.

Recipient

University

Research title

Anasse Bari

New York University

Predictive Analytics and Artificial Intelligence for Social Good

Andrew Gordon Wilson

New York University

Scalable Numerical Methods and Probabilistic Deep Learning with Applications to AutoML

Bo Li

University of Illinois at Urbana-Champaign

Trustworthy Machine Learning as Services via Robust AutoML and Knowledge Enhanced Logic Inference

Dawn Song

University of California, Berkeley

Protecting the Public Against AI-Generated Fakes

Dimosthenis Karatzas

Universitat Autónoma de Barcelona

Document Visual Question Answer (DocVQA) for Large-Scale Document Collections

Dit-Yan Yeung

Hong Kong University of Science and Technology

Temporally Misaligned Spatiotemporal Sequence Modeling

Lantao Liu

Indiana University Bloomington

Environment-Adaptive Sensing and Modeling using Autonomous Robots

Leonidas Guibas

Stanford University

Learning Canonical Spaces for Object-Centric 3D Perception

Maryam Rahnemoonfar

University of Maryland, Baltimore

Combining Model-Based and Data Driven Approaches to Study Climate Change via Amazon SageMaker

Mi Zhang

Michigan State University

DA-NAS: An AutoML Framework for Joint Data Augmentation and Neural Architecture Search

Michael P. Kelly

Washington University

Web-Based Machine Learning for Surgeon Benchmarking in Pediatric Spine Surgery

Ming Zhao

Arizona State University

Enabling Deep Learning across Edge Devices and Cloud Resources

Nianwen Xue

Brandeis University

AMR2KB: Construct a High-Quality Knowledge by Parsing Meaning Representations

Nicholas Chia

Mayo Clinic

Massively-Scaled Inverse Reinforcement Learning Approach for Reconstructing the Mutational History of Colorectal Cancer

Oswald Lanz

Fondazione Bruno Kessler

Structured Representation Learning for Video Recognition and Question Answering

Pierre Gentine

Columbia University

Learning Fires

Pratik Chaudhari

University of Pennsylvania

Offline and Off-Policy Reinforcement Learning

Pulkit Agrawal

Massachusetts Institute of Technology

Curiosity Baselines for the Reinforcement Learning Community

Quanquan Gu

University of California, Los Angeles

Towards Provably Efficient Deep Reinforcement Learning

Shayok Chakraborty

Florida State University

Active Learning with Imperfect Oracles

Soheil Feizi

University of Maryland, College Park

Explainable Deep Learning: Accuracy, Robustness and Fairness

Spyros Makradakis

University of Nicosia

Clustered Ensemble of Specialist Amazon GluonTS Models for Time Series Forecasting

Xin Jin

Johns Hopkins University

Making Sense of Network Performance for Distributed Machine Learning

Xuan (Sharon) Di

Columbia University

Multi-Autonomous Vehicle Driving Policy Learning for Efficient and Safe Traffic

Yi Yang

University of Technology Sydney

Efficient Video Analysis with Limited Supervision

Yun Raymond Fu

Northeastern University

Generative Feature Transformation for Multi-Viewed Domain Adaptation

Zhangyang (Atlas) Wang

Texas A&M University

Mobile-Captured Wound Image Analysis and Dynamic Modeling for Post-Discharge Monitoring of Surgical Site Infection

Zhi-Li Zhang

University of Minnesota

Universal Graph Embedding Neural Networks for Learning Graph-Structured Data

MLRA is now funded though the Amazon Research Awards (ARA) program. Please see the AWS AI call for proposal for more information.

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