Amazon Research Awards

Award Recipients

  • Shipra Agrawal

    Columbia University
    New Algorithmic Approaches for Reinforcement learning, with Application to Integer Programming
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  • Avishek Anand

    Leibniz Universitat Hannover
    Interpretability of Neural Rankers
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  • Nina Balcan

    Carnegie Mellon University
    Differentially-Private Learning for Massive Data Problems: Theory and Applications
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  • David Bamman

    University of California, Berkeley
    Natural Language Processing for Literary Texts
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  • Matthew Blaschko

    KU Leuven
    Low resource, highly-scalable discrete deep networks for the Amazon Bin Image Dataset Challenge
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  • Tevfik Bultan

    University of California, Santa Barbara
    Automatically Detecting Bugs in Identity and Access Management Policies
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  • Yinzhi Cao

    Lehigh University
    Cross-browser Fingerprinting: Attacks, Dynamics, and Detection
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  • Jia Deng

    University of Michigan
    Holistic Parsing of Human Activities in Videos
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  • Kevin Duh

    Johns Hopkins University
    Multi-objective Hyperparameter Search for Fast and Accurate Neural Machine Translation
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  • Jason Eisner

    Johns Hopkins University
    Continuous-Time Reinforcement Learning For Personalization
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  • Ron Fedkiw

    Stanford University
    Representation Learning for Cloth
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  • Maria Gini

    University of Minnesota
    Multi-robot Allocation and Scheduling of Tasks with Temporal Constraints
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  • Kristen Grauman

    University of Texas at Austin
    Explainable Visual Compatibility and Style Forecasting with Fashion Images
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  • Hannaneh Hajishirzi

    University of Washington
    Question Answering and Reasoning about Product Reviews
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  • Max Harper

    University of Minnesota
    Building Blocks for Natural Language Recommenders
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  • Kris Hauser

    Duke University
    Optimized Robotic Packing for Irregular and Diverse Objects
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  • Ralph Hollis

    Carnegie Mellon University
    Conversational Mobile Robots in Human Environments
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  • Piotr Indyk

    Massachusetts Institute of Technology
    Towards Accurate, Robust and Dynamic Metric Compression
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  • Thorsten Joachims

    Cornell University
    Unbiased Learning with Biased User Feedback
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  • Ross Knepper

    Cornell University
    Transferring Deep Reinforcement Learning Policies from Simulation to Real World for Robotic Manipulation of Soft Bodies
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  • Sven Koenig

    University of Southern California
    Multi-Agent Path Finding for Fulfillment Centers
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  • Oliver Kroemer

    Carnegie Mellon University
    Learning Recovery Skills for Robust Grasping and Manipulation
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  • Ranjitha Kumar

    University of Illinois at Urbana-Champaign
    An Experimentation Engine for Personal Fashion
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  • Svetlana Lazebnik

    University of Illinois at Urbana-Champaign
    Compositional Image Captioning Using High-Level Cues
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  • Chin-Hui Lee

    Georgia Institute of Technology
    Integrating Signal Pre-processing and Model Post-processing for Robust Single- and Multi-Channel Speech Recognition
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  • Jure Leskovec

    Stanford University
    MATLearn: Deep Representation Learning for Complex Malicious Behavior Detection
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  • Sergey Levine

    University of California, Berkeley
    Deep Reinforcement Learning for Dexterous Manipulation from Vision and Touch
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  • Percy Liang

    Stanford University
    Learning to Understand Natural Language Commands on Changing Websites
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  • Christopher Manning

    Stanford University
    Enabling Multilingual Language Understanding: Universal Typed Semantic Parsing
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  • Florian Metze

    Carnegie Mellon University
    Speech- and Image-to-Text for Video Captioning
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  • Sriraam Natarajan

    University of Texas at Dallas
    Guiding Probabilistic Learning in Structured Domains with Crowd-Sourced Inputs: Treating Humans as More Than Mere Labelers
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  • Ramakant Nevatia

    University of Southern California
    Open-vocabulary Activity Detection and Localization in Videos
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  • Robert Platt

    Northeastern University
    Better Robotic Manipulation via Deep Deictic Reinforcement Learning
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  • Theodoros Rekatsinas

    University of Wisconsin - Madison
    Statistical Learning and Probabilistic Inference Methods for Interactive Data Cleaning
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  • Alberto Rodriguez

    Massachusetts Institute of Technology
    Reactive Grasping with Tactile Reflexes
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  • Daniela Rus

    Massachusetts Institute of Technology
    Soft Hands for Packing and Unpacking
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  • Sebastian Scherer

    Carnegie Mellon University
    Multi-view 3D Object Detection and SLAM
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  • Cordelia Schmid

    Inria
    3D Understanding of Humans in Action from Real-World Videos
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  • Anshumali Shrivastava

    Rice University
    Scaling-up Machine Learning via Probabilistic Hashing
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  • Justin Solomon

    Massachusetts Institute of Technology
    Large-Scale Geometrically-Structured Sampling
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  • Suvrit Sra

    Massachusetts Institute of Technology
    Variable precision scalable nonconvex optimization
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  • Siddartha Srinivasa

    University of Washington
    Learning to Close the Gap between Simulators and Reality for Robotic Manipulation under Clutter and Uncertainty
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  • Torsten Suel

    New York University
    Exploring Index Tiering Methods for General and E-Commerce Search
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  • Charles Sutton

    University of Edinburgh
    DeepClean: Deep Learning for Inferring Data Cleaning Scripts
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  • Russ Tedrake

    Massachusetts Institute of Technology
    Robust Multi-Modal Perception for Manipulation in Clutter
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  • Frederico Tombari

    TU Munich
    Fully-monocular dense semantic SLAM for persistent and long-scene understanding
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  • Olga Vechtomova

    University of Waterloo
    Task-based latent semantic information retrieval
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  • Eugene Wu

    Columbia University
    Interactive Matcher Debugging via Adversarial Generation
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  • Luke Zettemoyer

    University of Washington
    Cross Sentence QA-SRL: Data and Algorithms for Recovering Implicit Semantic Relationships in Text
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  • Dhruv Batra

    Georgia Tech, USA
    Visual Dialog
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  • Matthew Blaschko

    University of Leuven, Belgium
    Co-regularization and Deep, Weakly-Supervised Segmentation of the Amazon Bin Image Data Set
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  • David Chiang

    University of Notre Dame, USA
    New Directions for Whole-Sentence Training of Neural Translation Models
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  • Hal Daume

    Univerity of Maryland, US
    Neural Machine Translation from Weak User Feedback
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  • Desmond Elliott

    University of Amsterdam, Holland
    Effective Approaches to Multitask Multimodal Translation
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  • Sanja Fidler

    University of Toronto, Canada
    Towards Natural Online Clothing Retail
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  • Kristen Grauman

    University of Texas, US
    Visual Style and Subtleties: Attributes for Search and Recommendation in Fashion Images
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  • Marcin Junczys-Dowmunt

    University of Edinburgh, UK
    Deployment-ready Open-source Neural Machine Translation
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  • Bastian Leibe

    RWTH Aachen, Germany
    End-to-End Deep Learning for Human Pose Estimation in Video
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  • Adam Lopez

    University of Edinburgh, UK
    Machine Translation on GPUs
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  • Graham Neubig

    Carnegie Mellon University, US
    Unified Neural Models of Morphological Analysis and Generation
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  • Devi Parikh

    Georgia Tech, USA
    Counting Everyday Objects in Everyday Scenes
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  • Maryam Rahnemoonfar

    Texas A&M University-Corpus Christi, USA
    RT - HPC: Real Time Heterogeneous Product Counting on Amazon Bin Image Dataset based on Deep Learning
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  • Cordelia Schmid

    Inria, France
    3D Human Action Recognition from Monocular RGB Videos
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  • Lucia Specia

    University of Sheffield, UK
    Predicting Relevance and Quality of Machine Translation for Product Reviews
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  • Graham Taylor

    University of Guelph, Canada
    Parametrizing input to a deep learning architecture
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  • Antonio Torralba

    Massachusetts Institute of Technology, USA
    Learning vision and language by watching movies and reading books
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  • Raquel Urtasun

    University of Toronto, Canada
    Holistic Deep Scene Parsing of Amazon Fulfillment Centers
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  • Chris Callison-Burch

    University of Pennsylvania, USA
    Low Resource Machine Translation via Matrix Factorization
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  • Marine Carpuat

    University of Maryland, USA
    Modeling Divergence in Bilingual Sentence Pairs for Machine Translation
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  • Kenneth Heafield

    University of Edinburgh, UK
    Faster Decoding and Better Features via Local Coarse-to-Fine
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  • Philipp Koehn

    Johns Hopkins University, USA
    Efficient High-Speed Search for Phrase-Based Statistical Machine Translation
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  • Matt Post

    Johns Hopkins University, USA
    Translation into morphologically rich languages with source-side annotations
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  • Stefan Riezler

    Heidelberg University, Germany
    Multimodal Pivots for Low Resource Machine Translation in E-Commerce Localization
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  • Lane Schwartz

    University of Illinois at Urbana-Champaign, USA
    Simple and Reliable Workflow management for replicable scientific computing
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