Fall 2021 and winter 2022 Amazon Research Awards recipients announced

Awardees represent more than 30 universities in eight countries. Recipients have access to Amazon public datasets, along with AWS AI/ML services and tools.

The Amazon Research Awards is a program that provides unrestricted funds and AWS Promotional Credits to academic researchers investigating research topics across a number of disciplines.

Today, we’re publicly announcing 51 award recipients who represent 37 universities in eight countries.

This announcement includes awards funded under the fall 2021 AWS AI and winter 2022 Alexa: Fairness in AI call for proposals. Proposals were reviewed for the quality of their scientific content, their creativity, and their potential to impact both the research community and society more generally. Theoretical advances, creative new ideas, and practical applications were all considered.

Recipients have access to more than 300 Amazon public datasets and can utilize AWS AI/ML services and tools through their AWS Promotional Credits. Recipients also are assigned an Amazon research contact who offers consultation and advice, along with opportunities to participate in Amazon events and training sessions.

Additionally, Amazon encourages the publication of research results, presentations of research at Amazon offices worldwide, and the release of related code under open-source licenses.

“Given the ubiquity of machine learning in our daily lives, ensuring that the experiences are fair and equitable has never been more important,” said Rahul Gupta, a senior manager of applied science with Alexa AI. “The breadth of expertise among the 2022 Amazon Research Awards recipients highlights Amazon’s commitment to trustworthy AI research and will bring together experts who are committed to solving intricate, yet important, problems.”

ARA funds proposals up to four times a year in a variety of research areas. Applicants are encouraged to visit the ARA call for proposals page for more information or send an email to be notified of future open calls.

The table below lists, in alphabetical order, fall 2021 AWS AI and winter 2022 Alexa: Fairness in AI cycle call-for-proposal recipients.

RecipientUniversityResearch title
Jacob AndreasMassachusetts Institute of TechnologyNatural Language Summaries of Deep Networks and Decisions
Elias BareinboimColumbia UniversityApproximate Causal Inference and Decision-Making
Luisa BentivogliFondazione Bruno KesslerBias Mitigation and Gender Neutralization Techniques for Automatic Translation
Adel BibiUniversity of OxfordRandomized Smoothing: Future Directions and Extensions
Peter BrusilovskyUniversity of PittsburghInvestigating and Evaluating Exploratory Recommender Systems
Snigdha ChaturvediUniversity of North Carolina at Chapel HillTask-agnostic Learning of Fair Text Representations and their Application in Natural Language Generation
Tianyi ChenRensselaer Polytechnic InstituteAutomating Decentralized Machine Learning via Bilevel Optimization
Ashok CutkoskyBoston UniversityPrivate Non-Convex Optimization via Momentum
Bhuwan DhingraDuke UniversityLong-form Question Answering via Collaborative Writing
Yufei DingUniversity of California, Santa BarbaraTensor-centric Acceleration Framework for Large-Scale Deep-Learning Recommendation Model on GPU Clouds
Yonina EldarMassachusetts Institute of Technology/Weizmann Institute of ScienceEfficient and Interpretable Deep Learning for Low Cost Ultrasound Imaging
Ferdinando FiorettoSyracuse UniversityToward Understanding the Unintended Disparate Impacts of Differentially Private Machine Learning Systems
David ForsythUniversity of Illinois Urbana-ChampaignLearning and Evaluating Object Detectors in the All-Novel-Class Regime
Dan GoldwasserPurdue University, West LafayetteUnderstanding Socially Grounded Language using Contextualized Discourse Embedding
Hui GuanUniversity of Massachusetts AmherstGroot: A GPU-Resident System for Efficient Graph Machine Learning
Callie HaoGeorgia Institute of TechnologyGeneralizable Zero-shot Auto-tuning for Efficient Deep Learning Workloads Delivery Co-learned with Neural Architecture Search
Wen-mei HwuUniversity of Illinois, Urbana–ChampaignDesign and Implementation of Storage-Scale Tensors for Efficient GNN Training
Yani IoannouUniversity of CalgaryAddressing Catastrophic Forgetting with Dynamic Sparse Training
Yangfeng JiUniversity of VirginiaBuilding Conversational Agents with Limited Resources
Zhihao JiaCarnegie Mellon UniversityTowards Affordable and Accessible ML by Leveraging Heterogeneous Spot Instances
Preethi JyothiIndian Institute of Technology BombayTowards Fairness in Speech Recognition using Targeted Subset Selection and Active Semi-supervised Learning
Dimosthenis KaratzasAutonomous University of BarcelonaMultipage and Multilingual Document Visual Question Answering
Parisa KordjamshidiMichigan State UniversityNatural Language Instruction Following in Realistic Visual Environments
Jana KoseckaGeorge Mason UniversityHand Shape Modeling for American Sign Language Recognition
Jing (Jane) LiUniversity of PennsylvaniaHDIBench: An End-to-End Benchmark for High-Dimensional Data Indexing and Searching
Sharon Yixuan LiUniversity of Wisconsin–MadisonUncertainty-aware Deep Learning for Reliable Decision Making in an Open World
Rada MihalceaUniversity of MichiganCommunity-aware Product Question Generation
Hongseok NamkoongColumbia UniversityDistributionally Robust Deep Learning Using Pre-trained Models
Shirui PanGriffith UniversityEffective Multi-Task Self-Supervised Learning for Graph Anomaly Detection
Nicolas PapernotUniversity of TorontoCharacterizing the Privacy Attack Surface of Machine Learning
Yifan PengWeill Cornell MedicineModeling longitudinal EHR to compose interpretable, deep knowledge-enhanced radiology reports
Christopher PottsStanford UniversityCausal abstractions of neural networks: Towards more explainable models and generalization guarantees
Saurabh PrasadUniversity Of HoustonSteerable Sparse Deep Neural Networks and Knowledge Transfer for Robust GeoAI
Pradeep RavikumarCarnegie Mellon UniversityCausal + Deep Out-of-Distribution Learning
Xiang RenUniversity of Southern CaliforniaGenerating and Utilizing Explanations for Human-in-the-Loop Language Model Refinement
Andrej RisteskiCarnegie Mellon UniversityCausal + Deep Out-of-Distribution Learning
Marco SerafiniUniversity of Massachusetts AmherstGroot: A GPU-Resident System for Efficient Graph Machine Learning
Vatsal SharanUniversity of Southern CaliforniaActionable Insights at Scale: Certified Anomaly Detection for Data-Intensive Systems
George ShihWeill Cornell MedicineModeling longitudinal EHR to compose interpretable, deep knowledge-enhanced radiology reports
Shashank SrivastavaUniversity of North Carolina At Chapel HillLearning from Natural Language Explanations for the Long Tail
Philip TorrUniversity of OxfordRandomized Smoothing: Future Directions and Extensions
Yuxiong WangUniversity of Illinois At Urbana–ChampaignLearning and Evaluating Object Detectors in the All-Novel-Class Regime
Fei WangCornell UniversityHigh-Throughput Drug Repurposing with Real World Data Enhanced with Biomedical Knowledge
Shinji WatanabeCarnegie Mellon UniversityNon-Autoregressive Conversational Speech Recognition
Yang XuUniversity of TorontoDeveloping machine comprehension and fairness toward informal language
Carl YangEmory UniversityFederated Learning on Graph Data: Utility, Efficiency, and Privacy
Diyi YangGeorgia Institute of TechnologyLearning Continually and Adaptatively for Natural Language Processing
Tao YuUniversity of Hong KongScalable Conversational Structured Knowledge Grounding with a Unified Language Model
Bin YuUC BerkeleyInterpretable and Stable AutoML
Bolei ZhouUniversity of California, Los AngelesImproving Out-of-Distribution Generalization through Steerable Generative Modeling.
Research areas
  • Applied machine learning
  • Machine learning algorithms and theory
  • Fairness in artificial intelligence
  • Natural language processing

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