Earlier this spring, Amazon notified grant applicants that they were recipients of the 2019 Amazon Research Awards, a grant program that provides up to $80,000 in cash and $20,000 in AWS Promotional Credits to academic researchers investigating topics across 11 focus areas. Today, we’re publicly announcing the 51 award recipients who represent 39 universities in 10 countries. The 2019 awards averaged $72,000 in cash awards and $15,000 in AWS Promotional Credits in support of each research project. Each grant is intended to support the work of one to two graduate students or postdoctoral students for one year, under the supervision of a faculty member.
The 11 focus areas of this year’s research awards are computer vision; fairness in artificial intelligence; knowledge management and data quality; machine learning algorithms and theory; natural-language processing; online advertising; operations research and optimization; personalization; robotics; search and information retrieval; and security, privacy, and abuse prevention.
Recipients can use more than 150 Amazon public data sets. Amazon encourages the publication of research results, researcher presentations at Amazon offices worldwide, and the release of related code under open-source licenses.
Each project is assigned an Amazon research contact who is available for consultation and supports the project’s progress.
“The Amazon Research Awards help fund outstanding, innovative research proposals across machine learning, robotics, operations research, and more, while helping strengthen connections between Amazon research teams, academic researchers, and their affiliated institutions,” said Swami Sivasubramanian, vice president of Amazon Machine Learning. “The breadth and depth of the research this year’s recipients will pursue is impressive and will lead to critical innovations for our customers and meaningful scientific advancements in each of the 11 focus areas.”
Grant proposals for 2020, which will be the program’s sixth year, will be accepted starting this fall. Please check back for more information this summer or send an email to be added to the 2020 Call For Proposal distribution list. Below is the list of 2019 award recipients, presented in alphabetical order.
| Recipient | University | Research title |
| Pulkit Agrawal | Massachusetts Institute of Technology | Continual Reinforcement Learning |
| James Allan | University of Massachusetts Amherst | Explanation of Product Facets for Conversational Search |
| Chris Amato | Northeastern University | Scalable and Robust Multi-Robot Coordination through High-Level Macro-Actions |
| Ashis G. Banerjee | University of Washington | Sparse, Deep and Persistent Visual Features Based 3D Object Detection and 6D Pose Estimation in Indoor Environments |
| Sven Behnke | University of Bonn | Learning Structured Scene Modeling and Physics-Based Prediction for Manipulation |
| François-Xavier Briol | University College London & the Alan Turing Institute | Transfer Learning for Numerical Integration in Expensive Machine Learning Systems |
| Flavio du Pin Calmon | Harvard University | Building the Foundations of Fair Machine Learning: From Information Theory to Federated Algorithms |
| Luca Carlone | Massachusetts Institute of Technology | Metric-Semantic SLAM for Long-Term Multi-Robot Deployment |
| Shayok Chakraborty | Florida State University | Deep Active Learning with Relative Label Feedback |
| Kai-Wei Chang | University of California Los Angeles | Learning Robust Contextual Language Encoders at Scale |
| Margarita Chli | ETH Zurich | Semantic-Aware Cloud-Aided Aerial Navigation for Drone Delivery |
| Jeff Dalton | University of Glasgow | Knowledge-Grounded Conversational Product Information Seeking |
| N. Lance Downing | Stanford University | DeepStroke: Improving Stroke Diagnosis with Deep Learning on NIH Stroke Scale Assessments |
| Luciana Ferrer | Computer Science Institute (ICC), UBA-CONICET | Representation Learning for Sound Understanding |
| Alexander Gammerman | Royal Holloway, University of London | Conformal Martingales for Change-Point Detection |
| Graeme Gange | Monash University | Robust Prioritised Planning for Multi-Agent Pathfinding |
| Itai Gurvich | Cornell University | Dynamic Resource Allocation to Heterogeneous Requests: Near Optimal, Computationally Light Policies |
| Kris Hauser | University of Illinois Urbana-Champaign | Robotic Packing of Novel and Non-Rigid Objects with Visuotactile Modeling |
| Daqing He | University of Pittsburgh | Transferable, Controllable, Applicable Keyphrase Generation |
| Jason Hong | Carnegie Mellon University | Designing Alternative Representations of Confusion Matrices to Evaluate Public Perceptions of Fairness in Machine Learning |
| Wendy Ju | Cornell Tech | Enabling Machines to Recognize and Repair Errors in Interaction |
| Sertac Karaman | Massachusetts Institute of Technology | Learning New Environments with a Tour: Depth and Pose Estimation through Informative Control Actions |
| Ioannis Karamouzas | Clemson University | Learning Efficient Multi-Robot Navigation from Human Crowd Data |
| Aryeh Kontorovich | Ben-Gurion University of the Negev | Advanced Proximity-Based Learning Toolkit for SageMaker |
| Oliver Kroemer | Carnegie Mellon University | Robust Manipulation Strategies for Delta-Robot Arrays |
| Beibei Li | Carnegie Mellon University | AI Agent for Targeted Promotion |
| Changliu Liu | Carnegie Mellon University | Hierarchical Motion Planning for Efficient and Provably Safe Human-Robot Interactions |
| Anirudha Majumdar | Princeton University | Force-Closure Nets: Manipulating Objects with Provable Guarantees on Generalization |
| Karthik Narasimhan | Princeton University | Towards Deeper, Broader and Human-Like Conversational Agents |
| Joseph P. Near | University of Vermont | Provable Fairness for Deep Learning via Automatic Differentiation |
| Priyadarshini Panda | Yale University | Adversarial Robustness with Efficiency-Driven Optimization of Deep Neural Networks |
| Guilherme Augsto Silva Pereira | West Virginia University | Parallel and Cloud Computing for Long-Term Robotics |
| Carlo Pinciroli | Worcester Polytechnic Institute | An Immersive Interface for Multi-User Supervision of Multi-Robot Operations |
| Ingmar Posner | University of Oxford | Compositional Deep Generative Models for Real-World Robot Perception and Manipulation |
| Amanda Prorok | University of Cambridge | Learning Explicit Communication for Multi-Robot Path Planning |
| Sebastian Risi | IT University of Copenhagen | Continually Learning Machines for Industrial Automation |
| Alessandro Rizzo | Politecnico di Torino | From Shortest to Safest Path Navigation: An AI-Powered Framework for Risk-Aware Autonomous Navigation of UASes |
| Nicolas Rojas | Imperial College London | Mechanical intelligence for in-hand manipulation |
| Daniela Rus | Massachusetts Institute of Technology | Series Elastic Magnetically Geared Robotic Actuators |
| Sanjay Sarma | Massachusetts Institute of Technology | Multi-modal Sensing for Material ID in Robotic Applications |
| Alex Schwing | University of Illinois Urbana-Champaign | Seeing the Unseen: Temporal Amodal Instance Level Video Object Segmentation |
| Roland Siegwart | ETH Zürich | Aerial Manipulation with an Omnidirectional Flying Platform |
| Niko Suenderhauf | Queensland University of Technology (QUT) | Learning Robotic Navigation and Interaction from Object-based Semantic Maps |
| Chenhao Tan | University of Colorado at Boulder | Actively Soliciting Human Explanations to Correct Biases in NLP Models |
| Jian Tang | HEC Montreal: Mila-Quebec AI Institute | Deep Active Learning for Graph Neural Networks |
| Marynel Vázquez | Yale University | Improving Social Robot Navigation via Group Interaction Awareness |
| Soroush Vosoughi | Dartmouth College | Protecting Online Anonymity Through Linguistic Style Transfer |
| Richard M. Voyles | Purdue University | Framework for One-Shot Learning of Contact-Intensive Tasks Through Coaching |
| May Dongmei Wang | Georgia Institute of Technology | Learning to Unlearn Biases in Recommendation Models |
| James Wang | The Pennsylvania State University | Advancing Automated Recognition of Emotion in the Wild |
| Xinyu Xing | The Pennsylvania State University | Fine-grained Malware Classification using Coarse-grained Labels |
