Amazon Research Awards

Award Recipients

  • ScientistUniversityTitle
  • Shipra AgrawalColumbia UniversityNew Algorithmic Approaches for Reinforcement learning, with Application to Integer Programming
  • Avishek AnandLeibniz Universitat HannoverInterpretability of Neural Rankers
  • Nina BalcanCarnegie Mellon UniversityDifferentially-Private Learning for Massive Data Problems: Theory and Applications
  • David BammanUniversity of California, BerkeleyNatural Language Processing for Literary Texts
  • Matthew BlaschkoKU LeuvenLow resource, highly-scalable discrete deep networks for the Amazon Bin Image Dataset Challenge
  • Tevfik BultanUniversity of California, Santa BarbaraAutomatically Detecting Bugs in Identity and Access Management Policies
  • Yinzhi CaoLehigh UniversityCross-browser Fingerprinting: Attacks, Dynamics, and Detection
  • Jia DengUniversity of MichiganHolistic Parsing of Human Activities in Videos
  • Kevin DuhJohns Hopkins UniversityMulti-objective Hyperparameter Search for Fast and Accurate Neural Machine Translation
  • Jason EisnerJohns Hopkins UniversityContinuous-Time Reinforcement Learning For Personalization
  • Ron FedkiwStanford UniversityRepresentation Learning for Cloth
  • Maria GiniUniversity of MinnesotaMulti-robot Allocation and Scheduling of Tasks with Temporal Constraints
  • Kristen GraumanUniversity of Texas at AustinExplainable Visual Compatibility and Style Forecasting with Fashion Images
  • Hannaneh HajishirziUniversity of WashingtonQuestion Answering and Reasoning about Product Reviews
  • Max HarperUniversity of MinnesotaBuilding Blocks for Natural Language Recommenders
  • Kris HauserDuke UniversityOptimized Robotic Packing for Irregular and Diverse Objects
  • Ralph HollisCarnegie Mellon UniversityConversational Mobile Robots in Human Environments
  • Piotr IndykMassachusetts Institute of TechnologyTowards Accurate, Robust and Dynamic Metric Compression
  • Thorsten JoachimsCornell UniversityUnbiased Learning with Biased User Feedback
  • Ross KnepperCornell UniversityTransferring Deep Reinforcement Learning Policies from Simulation to Real World for Robotic Manipulation of Soft Bodies
  • Sven KoenigUniversity of Southern CaliforniaMulti-Agent Path Finding for Fulfillment Centers
  • Oliver KroemerCarnegie Mellon UniversityLearning Recovery Skills for Robust Grasping and Manipulation
  • Ranjitha KumarUniversity of Illinois at Urbana-ChampaignAn Experimentation Engine for Personal Fashion
  • Svetlana LazebnikUniversity of Illinois at Urbana-ChampaignCompositional Image Captioning Using High-Level Cues
  • Chin-Hui LeeGeorgia Institute of TechnologyIntegrating Signal Pre-processing and Model Post-processing for Robust Single- and Multi-Channel Speech Recognition
  • Jure LeskovecStanford UniversityMATLearn: Deep Representation Learning for Complex Malicious Behavior Detection
  • Sergey LevineUniversity of California, BerkeleyDeep Reinforcement Learning for Dexterous Manipulation from Vision and Touch
  • Percy LiangStanford UniversityLearning to Understand Natural Language Commands on Changing Websites
  • Christopher ManningStanford UniversityEnabling Multilingual Language Understanding: Universal Typed Semantic Parsing
  • Florian MetzeCarnegie Mellon UniversitySpeech- and Image-to-Text for Video Captioning
  • Sriraam NatarajanUniversity of Texas at DallasGuiding Probabilistic Learning in Structured Domains with Crowd-Sourced Inputs: Treating Humans as More Than Mere Labelers
  • Ramakant NevatiaUniversity of Southern CaliforniaOpen-vocabulary Activity Detection and Localization in Videos
  • Robert PlattNortheastern UniversityBetter Robotic Manipulation via Deep Deictic Reinforcement Learning
  • Theodoros RekatsinasUniversity of Wisconsin - MadisonStatistical Learning and Probabilistic Inference Methods for Interactive Data Cleaning
  • Alberto RodriguezMassachusetts Institute of TechnologyReactive Grasping with Tactile Reflexes
  • Daniela RusMassachusetts Institute of TechnologySoft Hands for Packing and Unpacking
  • Sebastian SchererCarnegie Mellon UniversityMulti-view 3D Object Detection and SLAM
  • Cordelia SchmidInria3D Understanding of Humans in Action from Real-World Videos
  • Anshumali ShrivastavaRice UniversityScaling-up Machine Learning via Probabilistic Hashing
  • Justin SolomonMassachusetts Institute of TechnologyLarge-Scale Geometrically-Structured Sampling
  • Suvrit SraMassachusetts Institute of TechnologyVariable precision scalable nonconvex optimization
  • Siddartha SrinivasaUniversity of WashingtonLearning to Close the Gap between Simulators and Reality for Robotic Manipulation under Clutter and Uncertainty
  • Torsten SuelNew York UniversityExploring Index Tiering Methods for General and E-Commerce Search
  • Charles SuttonUniversity of EdinburghDeepClean: Deep Learning for Inferring Data Cleaning Scripts
  • Russ TedrakeMassachusetts Institute of TechnologyRobust Multi-Modal Perception for Manipulation in Clutter
  • Frederico TombariTU MunichFully-monocular dense semantic SLAM for persistent and long-scene understanding
  • Olga VechtomovaUniversity of WaterlooTask-based latent semantic information retrieval
  • Eugene WuColumbia UniversityInteractive Matcher Debugging via Adversarial Generation
  • Luke ZettemoyerUniversity of WashingtonCross Sentence QA-SRL: Data and Algorithms for Recovering Implicit Semantic Relationships in Text
  • ScientistUniversityTitle
  • Dhruv BatraGeorgia Tech, USAVisual Dialog
  • Matthew BlaschkoUniversity of Leuven, BelgiumCo-regularization and Deep, Weakly-Supervised Segmentation of the Amazon Bin Image Data Set
  • David ChiangUniversity of Notre Dame, USANew Directions for Whole-Sentence Training of Neural Translation Models
  • Hal DaumeUniverity of Maryland, USNeural Machine Translation from Weak User Feedback
  • Desmond ElliottUniversity of Amsterdam, HollandEffective Approaches to Multitask Multimodal Translation
  • Sanja FidlerUniversity of Toronto, CanadaTowards Natural Online Clothing Retail
  • Kristen GraumanUniversity of Texas, USVisual Style and Subtleties: Attributes for Search and Recommendation in Fashion Images
  • Marcin Junczys-DowmuntUniversity of Edinburgh, UKDeployment-ready Open-source Neural Machine Translation
  • Bastian LeibeRWTH Aachen, GermanyEnd-to-End Deep Learning for Human Pose Estimation in Video
  • Adam LopezUniversity of Edinburgh, UKMachine Translation on GPUs
  • Graham NeubigCarnegie Mellon University, USUnified Neural Models of Morphological Analysis and Generation
  • Devi ParikhGeorgia Tech, USACounting Everyday Objects in Everyday Scenes
  • Maryam RahnemoonfarTexas A&M University-Corpus Christi, USART - HPC: Real Time Heterogeneous Product Counting on Amazon Bin Image Dataset based on Deep Learning
  • Cordelia SchmidInria, France3D Human Action Recognition from Monocular RGB Videos
  • Lucia SpeciaUniversity of Sheffield, UKPredicting Relevance and Quality of Machine Translation for Product Reviews
  • Graham TaylorUniversity of Guelph, CanadaParametrizing input to a deep learning architecture
  • Antonio TorralbaMassachusetts Institute of Technology, USALearning vision and language by watching movies and reading books
  • Raquel UrtasunUniversity of Toronto, CanadaHolistic Deep Scene Parsing of Amazon Fulfillment Centers
  • ScientistUniversityTitle
  • Chris Callison-BurchUniversity of Pennsylvania, USALow Resource Machine Translation via Matrix Factorization
  • Marine CarpuatUniversity of Maryland, USAModeling Divergence in Bilingual Sentence Pairs for Machine Translation
  • Kenneth HeafieldUniversity of Edinburgh, UKFaster Decoding and Better Features via Local Coarse-to-Fine
  • Philipp KoehnJohns Hopkins University, USAEfficient High-Speed Search for Phrase-Based Statistical Machine Translation
  • Matt PostJohns Hopkins University, USATranslation into morphologically rich languages with source-side annotations
  • Stefan RiezlerHeidelberg University, GermanyMultimodal Pivots for Low Resource Machine Translation in E-Commerce Localization
  • Lane SchwartzUniversity of Illinois at Urbana-Champaign, USASimple and Reliable Workflow management for replicable scientific computing