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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
| Recipient | University | Research title |
| Jonathan Afilalo | McGill University | Coreslicer: deep learning of CT images for frailty assessment in clinical care |
| Saman Amarasinghe | Massachusetts Institute of Technology | Reimagining the compiler in the cloud |
| Akshay Chaudhari | Stanford University | Large-scale self-supervised learning for medical imaging |
| Soheil Feizi | University Of Maryland, College Park | Towards mitigating spurious correlations in deep learning |
| Aikaterini Fragkiadaki | Carnegie Mellon University | Analogical networks for continual memory-modulated visual learning and language understanding |
| Mark Gerstein | Yale University | Privacy-preserving storage, sharing, and analysis for genomics data |
| Joseph Gonzalez | University Of California, Berkeley | A unified platform for training and serving large models |
| Michael Gubanov | Florida State University | An interactive polygraph for robust access to scientific knowledge |
| Yan Huang | Carnegie Mellon University | Combating algorithmic bias inherited from human decision making: a human-AI perspective |
| CV Jawahar | The International Institute of Information Technology – Hyderabad | Deeper understanding of multilingual handwritten documents: from recognition to dialogues |
| Zhihao Jia | Carnegie Mellon University | Combining ML and systems optimizations for sustainable and affordable ML |
| Daniel Khashabi | Johns Hopkins University | Crowdsourcing with machine backbone |
| Rahul Krishnan | University Of Toronto | Towards a learning healthcare system |
| Anastasios Kyrillidis | Rice University | Efficient and affordable transformers for distributed platforms |
| Kevin Leach | Vanderbilt University | Documentnet: iterative data collection for building a robust document understanding dataset |
| Lei Li | University Of California, Santa Barbara | Real-time robust simultaneous interpretation with few samples |
| Xiaoyi Lu | University Of California, Merced | Scaling collective communication for distributed deep learning training |
| Yunan Luo | Georgia Institute of Technology | Calibrated and interpretable geometric deep learning for robust drug screening |
| Graham Neubig | Carnegie Mellon University | Towards more reliable and interpretable code language models |
| Qing Qu | University of Michigan, Ann Arbor | Principles of deep representation learning via neural collapse |
| Mirco Ravanelli | Concordia University | Toward empathetic conversational AI |
| Amit Roy-Chowdhury | University of California, Riverside | Exploring privacy in deep metric learning: applications in computer vision |
| Chirag Shah | University of Washington | Fairness as a service: operationalizing fairness in search and recommendation applications through a novel multi-objective optimization framework |
| Kristina Simonyan | Massachusetts Eye and Ear/Harvard Medical School | Machine learning for automated speech processing for real-time speech prosthesis in neurological disorders |
| Berrak Sisman | University of Texas, Dallas | Explainable AI for expressive voice synthesis |
| Dawn Song | University Of California, Berkeley | FedOps: an abstraction for trustworthy federated learning |
| Peter Spirtes | Carnegie Mellon University | System-level and long-term fairness through causal learning and reasoning |
| Ion Stoica | University Of California, Berkeley | A unified platform for training and serving large models |
| Vasileios Syrgkanis | Stanford University | Automating the causal machine learning pipeline |
| Carlo Tomasi | Duke University | Deep neural network classifiers with margins in input space |
| Yatish Turakhia | University Of California, San Diego | Machine learning enabled wastewater-based epidemiology |
| Xiaolong Wang | University of California, San Diego | Learning implicit neural foundation models |
| Neeraja Yadwadkar | University Of Texas, Austin | Easy-to-use and cost-efficient distributed inference serving |
| Hamed Zamani | University Of Massachusetts Amherst | On the optimization of retrieval-enhanced machine learning models |
| Ce Zhang | ETH Zurich | FedOps: an abstraction for trustworthy federated learning |
| Tianyi Zhang | Purdue University | Human-in-the-loop deep learning optimization for better usability, transparency, and user trust |
| Yiying Zhang | University Of California, San Diego | Training deep neural networks with “zero” activations |
| Jishen Zhao | University Of California, San Diego | Semantic-informed document structure recognition with large language models |
| Ben Zhao | University Of Chicago | Digital forensics for deep neural networks |
| Heather Zheng | University of Chicago | Digital forensics for deep neural networks |
| Jun-Yan Zhu | Carnegie Mellon University | Compositional personalization of large-scale generative models |
| Jia Zou | Arizona State University | A compilation framework for accelerating machine learning inference queries |
Amazon Sustainability
| Recipient | University | Research title |
| Vikram Iyer | University of Washington | Computational design and circular fabrication for sustainable electronics |
| Adriana Schulz | University of Washington | Computational design and circular fabrication for sustainable electronics |
| Mari Winkler | University of Washington | A novel bioreactor platform for continuous high‐rate bio-production |
Automated Reasoning
| Recipient | University | Research title |
| Maria Paola Bonacina | Università degli Studi di Verona | Advances in conflict-driven SATisfiability modulo theories and assignments |
| Ahmed Bouajjani | Universite Paris-Cite | Safe composition of distributed off-the-shelf components |
| Martin Nyx Brain | City, University Of London | Snowshoes: overapproximating code footprints for safe program exploration |
| Anton Burtsev | University Of Utah | Atmosphere: leveraging language safety and operating system design for verification |
| Alastair Donaldson | Imperial College London | DafnyDefender: automated testing for the Dafny ecosystem |
| Francois Dupressoir | University Of Bristol | Formosa cryptography: computer-aided reasoning for high-assurance cryptographic design and engineering |
| Gidon Ernst | Ludwig Maximilian University of Munich | Security specifications for Dafny |
| Pascal Fontaine | University of Liège | SMT: modules, formats, and standards |
| Jeffrey Foster | Tufts University | Automated testing of external methods in Dafny |
| Sicun Gao | University Of California, San Diego | Monte Carlo tree methods for decision-making in dReal |
| Philippa Gardner | Imperial College London | Gillian-Rust: unbounded verification for unsafe rust code |
| Limin Jia | Carnegie Mellon University | Enabling one-line rust verification with program synthesis |
| Patrick Lam | University Of Waterloo | Statically inferring contracts from assertions & tests |
| Aravind Machiry | Purdue University | Security verification and hardening of CI workflows |
| Anders Møller | Aarhus University | Securing node.js programs with static resource analysis |
| Magnus Myreen | Chalmers University Of Technology | Compiling Dafny to CakeML |
| ThanhVu Nguyen | George Mason University | Scalable and precise DNN constraint solving with abstraction and conflict clause learning |
| Burcu Kulahcioglu Ozkan | Delft University of Technology | Coverage-directed randomized testing of distributed systems |
| Bryan Parno | Carnegie Mellon University | Verus: developing provably correct and reliable rust code |
| Corina Pasareanu | Carnegie Mellon University | Enabling one-line rust verification with program synthesis |
| Ruzica Piskac | Yale University | Formalizing FISA: using automated reasoning to formalize legal reasoning |
| Elizabeth Polgreen | University of Edinburgh | Automated and provably correct code modernization |
| Fred Schneider | Cornell University | Using non-deterministic executable specification to test properties that relate executions |
| Scott Shapiro | Yale University | Formalizing FISA: using automated reasoning to formalize legal reasoning |
| Marc Shapiro | INRIA & Sorbonne Universite Paris | Safe composition of distributed off-the-shelf components |
| Alexandra Silva | Cornell University | Automated reasoning for correctness and incorrectness |
| Yakir Vizel | Technion – Israel Institute Of Technology | Lazy and incremental framework for solving CHCs |
| Florian Zuleger | Technische Universität Wien | Automated cost analysis of data structures |
Prime Video
| Recipient | University | Research title |
| David Bull | University of Bristol | Generic deep video quality assessment in the extended parameter space |
| Eamonn Keogh | University of California Riverside | A proposal to make any time series anomaly detection algorithm faster, more accurate and more practical |
| Xiaorui Liu | North Carolina State University | Deep reinforcement learning for the mixed ranking of recommendations and advertisements with page-wise display |
| Jiliang Tang | Michigan State University | Deep reinforcement learning for the mixed ranking of recommendations and advertisements with page-wise display |
| Hanghang Tong | University of Illinois Urbana-Champaign | Graph algorithms for personalized recommendation |
| Fan Zhang | University of Bristol | Generic deep video quality assessment in the extended parameter space |
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