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 53 award recipients who represent 38 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.
Recipient |
University |
Research title |
Jacob Andreas |
Massachusetts Institute of Technology |
Natural Language Summaries of Deep Networks and Decisions |
Elias Bareinboim |
Columbia University |
Approximate Causal Inference and Decision-Making |
Luisa Bentivogli |
Fondazione Bruno Kessler |
Bias Mitigation and Gender Neutralization Techniques for Automatic Translation |
Adel Bibi |
University of Oxford |
Randomized Smoothing: Future Directions and Extensions |
Peter Brusilovsky |
University of Pittsburgh |
Investigating and Evaluating Exploratory Recommender Systems |
Marine Carpuat |
University of Maryland, College Park |
Model Introspection for Detecting Hallucinations in Neural Machine Translation |
Snigdha Chaturvedi |
University of North Carolina at Chapel Hill |
Task-agnostic Learning of Fair Text Representations and their Application in Natural Language Generation |
Tianyi Chen |
Rensselaer Polytechnic Institute |
Automating Decentralized Machine Learning via Bilevel Optimization |
Ashok Cutkosky |
Boston University |
Private Non-Convex Optimization via Momentum |
Bhuwan Dhingra |
Duke University |
Long-form Question Answering via Collaborative Writing |
Yufei Ding |
University of California, Santa Barbara |
Tensor-centric Acceleration Framework for Large-Scale Deep-Learning Recommendation Model on GPU Clouds |
Yonina Eldar |
Massachusetts Institute of Technology/Weizmann Institute of Science |
Efficient and Interpretable Deep Learning for Low Cost Ultrasound Imaging |
Ferdinando Fioretto |
Syracuse University |
Toward Understanding the Unintended Disparate Impacts of Differentially Private Machine Learning Systems |
David Forsyth |
University of Illinois Urbana-Champaign |
Learning and Evaluating Object Detectors in the All-Novel-Class Regime |
Tom Goldstein |
University of Maryland |
Automated and Efficient Graph Algorithms with AutoGluon and DGL Integration |
Dan Goldwasser |
Purdue University, West Lafayette |
Understanding Socially Grounded Language using Contextualized Discourse Embedding |
Hui Guan |
University of Massachusetts Amherst |
Groot: A GPU-Resident System for Efficient Graph Machine Learning |
Callie Hao |
Georgia Institute of Technology |
Generalizable Zero-shot Auto-tuning for Efficient Deep Learning Workloads Delivery Co-learned with Neural Architecture Search |
Wen-mei Hwu |
University of Illinois, Urbana–Champaign |
Design and Implementation of Storage-Scale Tensors for Efficient GNN Training |
Yani Ioannou |
University of Calgary |
Addressing Catastrophic Forgetting with Dynamic Sparse Training |
Yangfeng Ji |
University of Virginia |
Building Conversational Agents with Limited Resources |
Zhihao Jia |
Carnegie Mellon University |
Towards Affordable and Accessible ML by Leveraging Heterogeneous Spot Instances |
Preethi Jyothi |
Indian Institute of Technology Bombay |
Towards Fairness in Speech Recognition using Targeted Subset Selection and Active Semi-supervised Learning |
Dimosthenis Karatzas |
Autonomous University of Barcelona |
Multipage and Multilingual Document Visual Question Answering |
Parisa Kordjamshidi |
Michigan State University |
Natural Language Instruction Following in Realistic Visual Environments |
Jana Kosecka |
George Mason University |
Hand Shape Modeling for American Sign Language Recognition |
Jing (Jane) Li |
University of Pennsylvania |
HDIBench: An End-to-End Benchmark for High-Dimensional Data Indexing and Searching |
Sharon Yixuan Li |
University of Wisconsin–Madison |
Uncertainty-aware Deep Learning for Reliable Decision Making in an Open World |
Rada Mihalcea |
University of Michigan |
Community-aware Product Question Generation |
Hongseok Namkoong |
Columbia University |
Distributionally Robust Deep Learning Using Pre-trained Models |
Shirui Pan |
Griffith University |
Effective Multi-Task Self-Supervised Learning for Graph Anomaly Detection |
Nicolas Papernot |
University of Toronto |
Characterizing the Privacy Attack Surface of Machine Learning |
Yifan Peng |
Weill Cornell Medicine |
Modeling longitudinal EHR to compose interpretable, deep knowledge-enhanced radiology reports |
Christopher Potts |
Stanford University |
Causal abstractions of neural networks: Towards more explainable models and generalization guarantees |
Saurabh Prasad |
University Of Houston |
Steerable Sparse Deep Neural Networks and Knowledge Transfer for Robust GeoAI |
Pradeep Ravikumar |
Carnegie Mellon University |
Causal + Deep Out-of-Distribution Learning |
Xiang Ren |
University of Southern California |
Generating and Utilizing Explanations for Human-in-the-Loop Language Model Refinement |
Andrej Risteski |
Carnegie Mellon University |
Causal + Deep Out-of-Distribution Learning |
Marco Serafini |
University of Massachusetts Amherst |
Groot: A GPU-Resident System for Efficient Graph Machine Learning |
Matteo Sesia |
University of Southern California |
CONFORMALIZED LEARNING FOR UNCERTAINTY-AWARE AI |
Vatsal Sharan |
University of Southern California |
Actionable Insights at Scale: Certified Anomaly Detection for Data-Intensive Systems |
George Shih |
Weill Cornell Medicine |
Modeling longitudinal EHR to compose interpretable, deep knowledge-enhanced radiology reports |
Shashank Srivastava |
University of North Carolina At Chapel Hill |
Learning from Natural Language Explanations for the Long Tail |
Philip Torr |
University of Oxford |
Randomized Smoothing: Future Directions and Extensions |
Yuxiong Wang |
University of Illinois At Urbana–Champaign |
Learning and Evaluating Object Detectors in the All-Novel-Class Regime |
Fei Wang |
Cornell University |
High-Throughput Drug Repurposing with Real World Data Enhanced with Biomedical Knowledge |
Shinji Watanabe |
Carnegie Mellon University |
Non-Autoregressive Conversational Speech Recognition |
Yang Xu |
University of Toronto |
Developing machine comprehension and fairness toward informal language |
Carl Yang |
Emory University |
Federated Learning on Graph Data: Utility, Efficiency, and Privacy |
Diyi Yang |
Georgia Institute of Technology |
Learning Continually and Adaptatively for Natural Language Processing |
Tao Yu |
University of Hong Kong |
Scalable Conversational Structured Knowledge Grounding with a Unified Language Model |
Bin Yu |
UC Berkeley |
Interpretable and Stable AutoML |
Bolei Zhou |
University of California, Los Angeles |
Improving Out-of-Distribution Generalization through Steerable Generative Modeling. |
