79 Amazon Research Awards recipients announced

Awardees, who represent 54 universities in 14 countries, have access to Amazon public datasets, along with AWS AI/ML services and tools.

Amazon Research Awards (ARA) provides unrestricted funds and AWS Promotional Credits to academic researchers investigating various research topics in multiple disciplines. This cycle, ARA received many excellent research proposals from across the world and today is publicly announcing 79 award recipients who represent 54 universities in 14 countries.

This announcement includes awards funded under four call for proposals during the fall 2022 cycle: AWS AI, Automated Reasoning, Prime Video, and Sustainability. Proposals were reviewed for the quality of their scientific content and their potential to impact both the research community and society.

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.

"Complexities of AI/ML challenges at scale often intersect more than one discipline and require creative and diverse approaches to tackle these issues," said Arash Nourian, AWS general manager, Machine Learning Engines. "I was amazed by the diversity of disciplines and the scientific content of Awardee’s submissions that collectively could represent significant potential impact on both the AI/ML research community and society."

“The incredible response to Prime Video’s fall 2022 Call for Proposals is a testament to the exciting work the ARAs have inspired across four cutting-edge research categories,” said BA Winston, VP of Technology at Prime Video. “I am delighted by the winning proposals and look forward to the ongoing research across several areas in Prime Video that is helping us create even more impactful customer-obsessed technology.”

ARA funds proposals throughout the 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 tables below list, in alphabetical order, fall 2022 cycle call-for-proposal recipients, sorted by research area.


AWS AI - ARA fall 2022.png

RecipientUniversityResearch title
Jonathan AfilaloMcGill UniversityCoreslicer: deep learning of CT images for frailty assessment in clinical care
Saman AmarasingheMassachusetts Institute of TechnologyReimagining the compiler in the cloud
Akshay ChaudhariStanford UniversityLarge-scale self-supervised learning for medical imaging
Soheil FeiziUniversity Of Maryland, College ParkTowards mitigating spurious correlations in deep learning
Aikaterini FragkiadakiCarnegie Mellon UniversityAnalogical networks for continual memory-modulated visual learning and language understanding
Mark GersteinYale UniversityPrivacy-preserving storage, sharing, and analysis for genomics data
Joseph GonzalezUniversity Of California, BerkeleyA unified platform for training and serving large models
Michael GubanovFlorida State UniversityAn interactive polygraph for robust access to scientific knowledge
Yan HuangCarnegie Mellon UniversityCombating algorithmic bias inherited from human decision making: a human-AI perspective
CV JawaharThe International Institute of Information Technology - HyderabadDeeper understanding of multilingual handwritten documents: from recognition to dialogues
Zhihao JiaCarnegie Mellon UniversityCombining ML and systems optimizations for sustainable and affordable ML
Daniel KhashabiJohns Hopkins UniversityCrowdsourcing with machine backbone
Rahul KrishnanUniversity Of TorontoTowards a learning healthcare system
Anastasios KyrillidisRice UniversityEfficient and affordable transformers for distributed platforms
Kevin LeachVanderbilt UniversityDocumentnet: iterative data collection for building a robust document understanding dataset
Lei LiUniversity Of California, Santa BarbaraReal-time robust simultaneous interpretation with few samples
Xiaoyi LuUniversity Of California, MercedScaling collective communication for distributed deep learning training
Yunan LuoGeorgia Institute of TechnologyCalibrated and interpretable geometric deep learning for robust drug screening
Graham NeubigCarnegie Mellon UniversityTowards more reliable and interpretable code language models
Qing QuUniversity of Michigan, Ann ArborPrinciples of deep representation learning via neural collapse
Mirco RavanelliConcordia UniversityToward empathetic conversational AI
Amit Roy-ChowdhuryUniversity of California, RiversideExploring privacy in deep metric learning: applications in computer vision
Chirag ShahUniversity of WashingtonFairness as a service: operationalizing fairness in search and recommendation applications through a novel multi-objective optimization framework
Kristina SimonyanMassachusetts Eye and Ear/Harvard Medical SchoolMachine learning for automated speech processing for real-time speech prosthesis in neurological disorders
Berrak SismanUniversity of Texas, DallasExplainable AI for expressive voice synthesis
Dawn SongUniversity Of California, BerkeleyFedOps: an abstraction for trustworthy federated learning
Peter SpirtesCarnegie Mellon UniversitySystem-level and long-term fairness through causal learning and reasoning
Ion StoicaUniversity Of California, BerkeleyA unified platform for training and serving large models
Vasileios SyrgkanisStanford UniversityAutomating the causal machine learning pipeline
Carlo TomasiDuke UniversityDeep neural network classifiers with margins in input space
Yatish TurakhiaUniversity Of California, San DiegoMachine learning enabled wastewater-based epidemiology
Xiaolong WangUniversity of California, San DiegoLearning implicit neural foundation models
Neeraja YadwadkarUniversity Of Texas, AustinEasy-to-use and cost-efficient distributed inference serving
Hamed ZamaniUniversity Of Massachusetts AmherstOn the optimization of retrieval-enhanced machine learning models
Ce ZhangETH ZurichFedOps: an abstraction for trustworthy federated learning
Tianyi ZhangPurdue UniversityHuman-in-the-loop deep learning optimization for better usability, transparency, and user trust
Yiying ZhangUniversity Of California, San DiegoTraining deep neural networks with "zero" activations
Jishen ZhaoUniversity Of California, San DiegoSemantic-informed document structure recognition with large language models
Ben ZhaoUniversity Of ChicagoDigital forensics for deep neural networks
Heather ZhengUniversity of ChicagoDigital forensics for deep neural networks
Jun-Yan ZhuCarnegie Mellon UniversityCompositional personalization of large-scale generative models
Jia ZouArizona State UniversityA compilation framework for accelerating machine learning inference queries

Amazon Sustainability

Amazon Sustainability ARA fall 2022.png

RecipientUniversityResearch title
Vikram IyerUniversity of WashingtonComputational design and circular fabrication for sustainable electronics
Adriana SchulzUniversity of WashingtonComputational design and circular fabrication for sustainable electronics
Mari WinklerUniversity of WashingtonA novel bioreactor platform for continuous high‐rate bio-production

Automated Reasoning

Automated Reasoning ARA fall 2022.png

RecipientUniversityResearch title
Maria Paola BonacinaUniversità degli Studi di VeronaAdvances in conflict-driven SATisfiability modulo theories and assignments
Ahmed BouajjaniUniversite Paris-CiteSafe composition of distributed off-the-shelf components
Martin Nyx BrainCity, University Of LondonSnowshoes: overapproximating code footprints for safe program exploration
Anton BurtsevUniversity Of UtahAtmosphere: leveraging language safety and operating system design for verification
Alastair DonaldsonImperial College LondonDafnyDefender: automated testing for the Dafny ecosystem
Francois DupressoirUniversity Of BristolFormosa cryptography: computer-aided reasoning for high-assurance cryptographic design and engineering
Gidon ErnstLudwig Maximilian University of MunichSecurity specifications for Dafny
Pascal FontaineUniversity of LiègeSMT: modules, formats, and standards
Jeffrey FosterTufts UniversityAutomated testing of external methods in Dafny
Sicun GaoUniversity Of California, San DiegoMonte Carlo tree methods for decision-making in dReal
Philippa GardnerImperial College LondonGillian-Rust: unbounded verification for unsafe rust code
Limin JiaCarnegie Mellon UniversityEnabling one-line rust verification with program synthesis
Patrick LamUniversity Of WaterlooStatically inferring contracts from assertions & tests
Aravind MachiryPurdue UniversitySecurity verification and hardening of CI workflows
Anders MøllerAarhus UniversitySecuring node.js programs with static resource analysis
Magnus MyreenChalmers University Of TechnologyCompiling Dafny to CakeML
ThanhVu NguyenGeorge Mason UniversityScalable and precise DNN constraint solving with abstraction and conflict clause learning
Burcu Kulahcioglu OzkanDelft University of TechnologyCoverage-directed randomized testing of distributed systems
Bryan ParnoCarnegie Mellon UniversityVerus: developing provably correct and reliable rust code
Corina PasareanuCarnegie Mellon UniversityEnabling one-line rust verification with program synthesis
Ruzica PiskacYale UniversityFormalizing FISA: using automated reasoning to formalize legal reasoning
Elizabeth PolgreenUniversity of EdinburghAutomated and provably correct code modernization
Fred SchneiderCornell UniversityUsing non-deterministic executable specification to test properties that relate executions
Scott ShapiroYale UniversityFormalizing FISA: using automated reasoning to formalize legal reasoning
Marc ShapiroINRIA & Sorbonne Universite ParisSafe composition of distributed off-the-shelf components
Alexandra SilvaCornell UniversityAutomated reasoning for correctness and incorrectness
Yakir VizelTechnion – Israel Institute Of TechnologyLazy and incremental framework for solving CHCs
Florian ZulegerTechnische Universität WienAutomated cost analysis of data structures

Prime Video

Prime Video ARA fall 2022.png

RecipientUniversityResearch title
David BullUniversity of BristolGeneric deep video quality assessment in the extended parameter space
Eamonn KeoghUniversity of California RiversideA proposal to make any time series anomaly detection algorithm faster, more accurate and more practical
Xiaorui LiuNorth Carolina State UniversityDeep reinforcement learning for the mixed ranking of recommendations and advertisements with page-wise display
Jiliang TangMichigan State UniversityDeep reinforcement learning for the mixed ranking of recommendations and advertisements with page-wise display
Hanghang TongUniversity of Illinois Urbana-ChampaignGraph algorithms for personalized recommendation
Fan ZhangUniversity of BristolGeneric deep video quality assessment in the extended parameter space

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