2019 Amazon Research Awards recipients announcement

Earlier this year, Amazon notified grant applicants who were recipients of the 2019 Amazon Research Awards.

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.

RecipientUniversityResearch title
Pulkit AgrawalMassachusetts Institute of TechnologyContinual Reinforcement Learning
James AllanUniversity of Massachusetts AmherstExplanation of Product Facets for Conversational Search
Chris AmatoNortheastern UniversityScalable and Robust Multi-Robot Coordination through High-Level Macro-Actions
Ashis G. BanerjeeUniversity of WashingtonSparse, Deep and Persistent Visual Features Based 3D Object Detection and 6D Pose Estimation in Indoor Environments
Sven BehnkeUniversity of BonnLearning Structured Scene Modeling and Physics-Based Prediction for Manipulation
François-Xavier BriolUniversity College London & the Alan Turing InstituteTransfer Learning for Numerical Integration in Expensive Machine Learning Systems
Flavio du Pin CalmonHarvard UniversityBuilding the Foundations of Fair Machine Learning: From Information Theory to Federated Algorithms
Luca CarloneMassachusetts Institute of TechnologyMetric-Semantic SLAM for Long-Term Multi-Robot Deployment
Shayok ChakrabortyFlorida State UniversityDeep Active Learning with Relative Label Feedback
Kai-Wei ChangUniversity of California Los AngelesLearning Robust Contextual Language Encoders at Scale
Margarita ChliETH ZurichSemantic-Aware Cloud-Aided Aerial Navigation for Drone Delivery
Jeff DaltonUniversity of GlasgowKnowledge-Grounded Conversational Product Information Seeking
N. Lance DowningStanford UniversityDeepStroke: Improving Stroke Diagnosis with Deep Learning on NIH Stroke Scale Assessments
Luciana FerrerComputer Science Institute (ICC), UBA-CONICETRepresentation Learning for Sound Understanding
Alexander GammermanRoyal Holloway, University of LondonConformal Martingales for Change-Point Detection
Graeme GangeMonash UniversityRobust Prioritised Planning for Multi-Agent Pathfinding
Itai GurvichCornell UniversityDynamic Resource Allocation to Heterogeneous Requests: Near Optimal, Computationally Light Policies
Kris HauserUniversity of Illinois Urbana-ChampaignRobotic Packing of Novel and Non-Rigid Objects with Visuotactile Modeling
Daqing HeUniversity of PittsburghTransferable, Controllable, Applicable Keyphrase Generation
Jason HongCarnegie Mellon UniversityDesigning Alternative Representations of Confusion Matrices to Evaluate Public Perceptions of Fairness in Machine Learning
Wendy JuCornell TechEnabling Machines to Recognize and Repair Errors in Interaction
Sertac KaramanMassachusetts Institute of TechnologyLearning New Environments with a Tour: Depth and Pose Estimation through Informative Control Actions
Ioannis KaramouzasClemson UniversityLearning Efficient Multi-Robot Navigation from Human Crowd Data
Aryeh KontorovichBen-Gurion University of the NegevAdvanced Proximity-Based Learning Toolkit for SageMaker
Oliver KroemerCarnegie Mellon UniversityRobust Manipulation Strategies for Delta-Robot Arrays
Beibei LiCarnegie Mellon UniversityAI Agent for Targeted Promotion
Changliu LiuCarnegie Mellon UniversityHierarchical Motion Planning for Efficient and Provably Safe Human-Robot Interactions
Anirudha MajumdarPrinceton UniversityForce-Closure Nets: Manipulating Objects with Provable Guarantees on Generalization
Karthik NarasimhanPrinceton UniversityTowards Deeper, Broader and Human-Like Conversational Agents
Joseph P. NearUniversity of VermontProvable Fairness for Deep Learning via Automatic Differentiation
Priyadarshini PandaYale UniversityAdversarial Robustness with Efficiency-Driven Optimization of Deep Neural Networks
Guilherme Augsto Silva PereiraWest Virginia UniversityParallel and Cloud Computing for Long-Term Robotics
Carlo PinciroliWorcester Polytechnic InstituteAn Immersive Interface for Multi-User Supervision of Multi-Robot Operations
Ingmar PosnerUniversity of OxfordCompositional Deep Generative Models for Real-World Robot Perception and Manipulation
Amanda ProrokUniversity of CambridgeLearning Explicit Communication for Multi-Robot Path Planning
Sebastian RisiIT University of CopenhagenContinually Learning Machines for Industrial Automation
Alessandro RizzoPolitecnico di TorinoFrom Shortest to Safest Path Navigation: An AI-Powered Framework for Risk-Aware Autonomous Navigation of UASes
Nicolas RojasImperial College LondonMechanical intelligence for in-hand manipulation
Daniela RusMassachusetts Institute of TechnologySeries Elastic Magnetically Geared Robotic Actuators
Sanjay SarmaMassachusetts Institute of TechnologyMulti-modal Sensing for Material ID in Robotic Applications
Alex SchwingUniversity of Illinois Urbana-ChampaignSeeing the Unseen: Temporal Amodal Instance Level Video Object Segmentation
Roland SiegwartETH ZürichAerial Manipulation with an Omnidirectional Flying Platform
Niko SuenderhaufQueensland University of Technology (QUT)Learning Robotic Navigation and Interaction from Object-based Semantic Maps
Chenhao TanUniversity of Colorado at BoulderActively Soliciting Human Explanations to Correct Biases in NLP Models
Jian TangHEC Montreal: Mila-Quebec AI InstituteDeep Active Learning for Graph Neural Networks
Marynel VázquezYale UniversityImproving Social Robot Navigation via Group Interaction Awareness
Soroush VosoughiDartmouth CollegeProtecting Online Anonymity Through Linguistic Style Transfer
Richard M. VoylesPurdue UniversityFramework for One-Shot Learning of Contact-Intensive Tasks Through Coaching
May Dongmei WangGeorgia Institute of TechnologyLearning to Unlearn Biases in Recommendation Models
James WangThe Pennsylvania State UniversityAdvancing Automated Recognition of Emotion in the Wild
Xinyu XingThe Pennsylvania State UniversityFine-grained Malware Classification using Coarse-grained Labels

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