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2020 Q1/Q2 AWS Machine Learning Research Awards recipients announced

The awards aim to advance machine learning by funding research and open-source projects, training students, and more. The recipients represent 21 universities in six countries.

By Amazon Research Awards team
February 15, 2021
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We are excited to announce 22 recipients from the AWS Machine Learning Research Awards (now part of Amazon Research Awards) 2020 Q1/Q2 call-for-proposal cycles. The recipients, representing 21 universities in six countries, aim to develop open-source tools and conduct 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. We are also pleased to work with universities in China, Hong Kong, and Taiwan for the first time.

The following are the 2020 Q1/Q2 recipients:

RecipientUniversityResearch TitleAward Year
Andrea VedaldiUniversity of OxfordLarge-Scale Understanding of Self-Supervised Image Feature Representation Learning2020 Q1
Chao ZhangGeorgia Institute of TechnologyEnabling Pre-Trained Language Models for Open, Low-Resource Information Extraction2020 Q1
Gennady Pekhimen-koUniversity of TorontoEfficient DNN Training at Scale: From Algorithms to Hardware2020 Q1
Irwin KingThe Chinese University of Hong KongGraph Neural Networks for Learning on Heterogeneous Graphs: Methods, Applications, and Tools2020 Q1
Jason HongCarnegie Mellon UniversityOrganizing Crowd Workers to Categorize Bias in ML Systems with Bias Bounties2020 Q1
Jiawei HanUniversity of Illinois at Urbana-ChampaignEmpower Heterogenous Information Network with Label Efficient Graph Representation Learning2020 Q1
Jonathan TamirThe University of Texas at AustinAI-driven Magnetic Resonance Imaging for Same-Day Point-of-Care Imaging and Diagnosis2020 Q1
Michael BronsteinImperial College LondonGeometric Deep Learning Model for Functional Protein Design2020 Q1
Peng GongTsinghua University21st Century Seasonal to Annual Global Land Cover and Land Use Dynamics: A Spatial- Temporal Cube Reconstruction Approach Using Amazon Web Services2020 Q1
Ying DingThe University of Texas at AustinI-RadioDiagno: Human-Centered AI Medical Imaging Diagnosis Tool2020 Q1
Yun-Nung (Vivian) ChenNational Taiwan UniversityTowards Robust Spoken Language Understanding2020 Q1
Zhaoran WangNorthwestern UniversityProvable Deep Reinforcement Learning in Real World: Efficient Exploration, Model-Based Learning, and Sim2Real2020 Q1
Zhou YuUniversity of California, DavisKnowledge Augmented Dialog Systems with Computational and Sample Efficiency2020 Q1
Anna KorhonenUniversity of CambridgeAI-assisted Functional Genomics2020 Q2
Hung-yi LeeNational Taiwan UniversitySpeech Processing Decathlon2020 Q2
Jonathan P. HowMassachusetts Institute of TechnologyFast Adaptation via Meta-Learning in Multiagent Reinforcement Learning2020 Q2
Liang ZhaoEmory UniversityDistributed Large‐scale Graph Deep Learning by Gradient‐free Optimization2020 Q2
Noah SnavelyCornell UniversityJoint Reasoning over Images, Language, and 3D2020 Q2
Qi (Rose) YuUniversity of California, San DiegoDeep Relational Forecasting for Dynamic Graphs2020 Q2
Xia NingThe Ohio State UniversitySynthesizability-Guided Molecular Graph Generation via Deep Learning2020 Q2
Xiangxiang ZengHunan UniversityGraph Neural Networks for Drug Repositioning2020 Q2
Yong YuShanghai Jiao Tong UniversityExploring Interaction Models on Graph Data and their Applications on Recommender Systems2020 Q2

Since 2017, AWS Machine Learning Research Awards (MLRA) has supported more than 190 research projects from 80 schools and research institutes in 15 countries, on topics such as ML algorithms, computer vision, natural language processing, medical research, neuroscience, social science, physics, and robotics.

In August 2020, MLRA and Amazon Research Awards (ARA) merged to make it easier for academic researchers to apply through a single submission system and a centrally managed website.

ARA is reviewing the proposals from 2020 call for proposals, and the decision letters will be sent out next month. Congratulations to the new recipients! We look forward to supporting your research.

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