Amazon Research Awards

Call for proposals

The 2018 Amazon Research Awards application period is now closed. By the end of January 2019, we will communicate the decision to the Principal Investigator listed on the proposal and announce the recipients on the ARA website.

Submission Requirements

Project proposals should be a maximum of 4 pages (single column, minimum 10 pt font), plus 1 page for references, plus CV. All content components (proposal, references, CV) should be composed into a single PDF file. We accept one submission per Principal Investigator.

Project proposals must include:

  1. Motivation for a specific research task.
  2. An outline of the approach, challenges and expected results.
  3. A proposed budget not exceeding $80,000 in cash and $20,000 in AWS Promotional Credits.
  4. CV of the leading faculty member. CV can be submitted as a link listed at the end of the proposal document.

Computer vision

  • Computational photography
  • Computer vision for apparel
  • Computer vision for robotics
  • Faces and gestures
  • Human body: detection, tracking, pose
  • Image and video captioning
  • Large-scale data annotation
  • Motion: segmentation, tracking
  • Recognition: categorization, detection, segmentation
  • 3D modeling: structure-from-motion, slam, stereo and reconstruction
  • Video understanding: actions, events
  • Visual search

Economics

  • Causality and ML
  • Economics of privacy and big data
  • Empirical IO
  • Hierarchical forecasting models
  • Intermittent demand models
  • Labor and personnel economics (firm’s perspective)
  • Mechanism design
  • Public finance, state and local taxes, tariffs
  • Quantitative marketing
  • Reduced form causal analysis/treatment effects
  • State space models
  • Time series forecasting
  • Transportation and logistics

Knowledge management and data quality

  • Data cleaning for machine learning
  • Graph mining from knowledge graphs and user behaviors
  • Knowledge embedding
  • Knowledge extraction from unstructured and semi-structured data
  • Knowledge verification
  • Knowledge-based search
  • Large-scale data alignment and integration
  • Leveraging structured knowledge in deep learning and recommendation
  • Quantitative and logical error detection

Machine learning algorithms and theory

  • Active learning
  • Data and resource efficient learning
  • Deep learning
  • Fair and interpretable learning
  • Meta-learning
  • Online and continual learning
  • Parallel and distributed Learning
  • Privacy preserving learning
  • Reinforcement learning
  • Representation learning
  • Transfer learning

Natural language processing

  • Advances in methods for estimating MT quality at run time
  • Advances in MT for noisy and user-generated content
  • Chatbots and dialogue systems
  • Detection of inappropriate content
  • Efficient training for neural MT
  • Explainability and robustness of NMT
  • Fact extraction from unstructured data
  • Fact verification and trustworthiness of text sources
  • Multitask and reinforcement learning for MT
  • Named Entity translation and transliteration
  • NLP Applications in Search
  • Question answering
  • Text Generation
  • Text Summarization
  • Using MT and parallel data for bootstrapping other NLP/NLU applications

Operations research and optimization

  • Assortment management
  • Management of warehouse operations
  • Marketplace design: incentives/policies for increasing efficiency and growth in a multi-agent marketplace
  • Strategic supply chain management: network design/topology
  • Tactical supply chain management: vendor management (including supplier contract negotiation and procurement), inventory buying, inventory deployment, demand fulfillment
  • Transportation: long-haul operations (including airline operations), last-mile operations

Personalization

  • Approaches to recommend products that uses customer's fashion preferences
  • Approaches to recommend products without behavior data ("cold start" item recommendations)
  • Content based approaches for estimating popularity of newly or yet-to-be released products
  • Item-to-item collaborative filtering using deep learning
  • Learning user preferences over ontologies/interests
  • Online experimentation approaches for selecting best recommendation strategies

Robotics

  • Autonomous navigation and mobility
  • Computer vision for object detection and pose estimation
  • Cloud-based robotics
  • Dexterous and reactive manipulation
  • Human machine interaction and collaboration
  • Machine learning for robotics
  • Motion planning
  • Multi-robot systems
  • Simulation for robotics
  • SLAM and perception for mobility

Search and information retrieval

  • Advances in ranking for tail (infrequent) ecommerce queries
  • Document quality techniques for search ranking features and spam detection
  • Evaluation of ecommerce search algorithms, offline and online
  • Index tiering based on advances in document quality assessment
  • Performance improvement of large machine learning systems for search
  • Product knowledge graph and navigation taxonomy construction and integration into ecommerce search algorithms
  • Spell checking, query normalization, query auto-complete

Security, privacy and abuse prevention

  • Bot and malware detection
  • Browser/device fingerprint and digital forensics
  • Early detection of emerging patterns with limited labeled data (one-shot-learning)
  • Fraud detection and prevention
  • Graph modeling (latent representations from a graph and anomaly detection)
  • Human-in-the-loop machine learning
  • Online and adaptive machine learning
  • Web behavioral modeling, online identity and password-less authentication

Speech

  • Advanced deep learning methods for speech recognition
  • ASR error detection and confidence estimation
  • Cross-lingual transfer learning
  • Emotion recognition
  • Keyword spotting
  • Media and recorded speech detection
  • Multi-lingual ASR
  • Real-time ASR in embedded/low resource applications
  • Robustness to microphone variation, microphone independent modeling
  • Speaker recognition and diarization
  • Speech recognition and speech end-pointing robustness to background speech, music, noise, reverberation
  • Spoken dialog systems and conversational understanding
  • Text to speech synthesis
  • Unsupervised training, adaptation in automatic speech recognition (ASR)
  • Very large vocabulary, domain-independent ASR

Eligibility

Full-time permanent faculty members of institutions granting PhD degrees in fields related to Machine Learning are eligible to apply.

Awards Structure

Awards are structured as one-year unrestricted gifts to academic institutions. Though the funding is not extendable, applicants can submit new proposals for subsequent calls.

Award Process

Project proposals are reviewed by an internal awards panel and the results are communicated to the applicants approximately three months after the submission deadline. Each project will be assigned an Amazon researcher contact. The university researchers are encouraged to maintain regular communication with this contact to discuss ongoing research and project progress. Researchers are also encouraged to publish the outcome of the project and commit any related code to open-source code repositories.