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2019 Amazon Research Awards recipients announcement

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

By Amazon Research Awards team
June 17, 2020
6 min read
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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.

2019 Amazon Research Award recipients ARA.png

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
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US, WA, Seattle
We are seeking a Senior Manager, Applied Science to build and lead the science organization across Agentic WorkSpaces. This is a foundational leadership role spanning the full portfolio — Personal, Applications, and Core, and the agentic surfaces (WS4Builders and WorkSpaces for Agents). You will hire, grow, and lead a team of applied scientists who define how we measure and improve the performance of AI agents and human-AI teams. A core part of the role is defining the science agenda itself — identifying which problems are most worth solving and where the highest-leverage bets lie. 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Set the bar for scientific talent, create the growth paths, and build the culture that makes AAWS a destination for the best agent and human-AI researchers. Own the science strategy across the portfolio. Direct the research agenda for how we measure and improve agents and human-AI teams: the benchmarks, task suites, and metrics (accuracy, cost-per-task, task completion, productivity) that turn subjective "it works" judgments into rigorous, reproducible measurement that gates what we ship. Define and drive high-leverage research directions. Work with your team to identify the problems most worth solving and shape the science agenda. Directions worth exploring might include how agents combine deterministic tool use (MCP) with visual reasoning from computer use; Organizational Intelligence and workflow learning (learning from expert recordings, voice annotations, and SOPs); and AI Agent Experience / AiAX (detecting when agents are stuck or degrading productivity and autonomously remediating) — these are illustrative starting points, and your team will weigh them against many other possibilities. Translate science into shipped product. Partner with engineering, product, and program leaders to move models, evaluation, and learning systems from prototype into a decade-old production service operating at massive scale, without compromising the reliability that customers depend on. Represent science in leadership and to customers. Be the scientific voice in org-level planning and roadmap decisions across AAWS, and engage directly with enterprise customers on how agent performance, safety, and human-AI productivity are measured and earned. Key job responsibilities Build and lead the applied science team. Hire, develop, and retain a high-caliber team of applied scientists spanning the Agentic WorkSpaces portfolio. Set the bar for scientific talent, create the growth paths, and build the culture that makes AAWS a destination for the best agent and human-AI researchers. Own the science strategy across the portfolio. Direct the research agenda for how we measure and improve agents and human-AI teams: the benchmarks, task suites, and metrics (accuracy, cost-per-task, task completion, productivity) that turn subjective "it works" judgments into rigorous, reproducible measurement that gates what we ship. Define and drive high-leverage research directions. Work with your team to identify the problems most worth solving and shape the science agenda. Directions worth exploring might include how agents combine deterministic tool use (MCP) with visual reasoning from computer use; Organizational Intelligence and workflow learning (learning from expert recordings, voice annotations, and SOPs); and AI Agent Experience / AiAX (detecting when agents are stuck or degrading productivity and autonomously remediating) — these are illustrative starting points, and your team will weigh them against many other possibilities. Translate science into shipped product. Partner with engineering, product, and program leaders to move models, evaluation, and learning systems from prototype into a decade-old production service operating at massive scale, without compromising the reliability that customers depend on. Represent science in leadership and to customers. 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Direct research on highly ambiguous, novel problems: Guide the team through foundational challenges in agent perception, reasoning, evaluation, reliability, and human-AI collaboration — problems where neither the approach nor the success criteria are pre-defined. Drive cross-organizational alignment: Work across partner teams (AgentCore, Bedrock model teams, Identity, Security, the MCP ecosystem) and across the Applied AI Solutions product portfolio, with product and engineering leadership, to ensure scientific decisions compose into a coherent product. Deliver measurable business impact: Ensure your team's research translates to customer outcomes: higher task accuracy, lower cost-per-action, faster time-to-production, measurable productivity for human-AI teams, and the trust that lets enterprises scale agent workflows. Establish scientific rigor and operational excellence: Set the standard for experimentation, evaluation, and reproducibility, and the mechanisms that keep the science organization productive and accountable. Advance the state of the art: Enable and champion contributions to the external technical community through publications, patents, and open-source work that position AWS as the leader in the science of secure agent-computer interaction and human-AI teamwork. About the team AWS Applied AI Solutions' (AAIS) vision is every business innovating with Amazon AI teammates. Our mission is to build delightful AI solutions that improve human capabilities and business outcomes. The Agentic WorkSpaces organization within AAIS envisions a world where people, teams, and AI collaborate securely from anywhere to create unprecedented value for every organization. We build lovable products that empower every business to unlock the full potential of human-AI teamwork, driving smarter decisions, greater creativity, more value, and faster innovation with confidence. Amazon Agentic WorkSpaces (AAWS) is building the world's most lovable, secure, and trusted always-on workspace where AI agents and humans work as partners behind enterprise-grade security. Our portfolio spans persistent desktops (Personal), application streaming (Applications), and Core, and is evolving into the governed operating environment for the hybrid workforce: humans get AI-native desktops for their role, and agents get governed desktops scoped to their task, with administrators managing both as one. This surface includes WS4Builders (an AI-native environment for builders) and WorkSpaces for Agents (W4A) — enabling AI agents to work the way humans do, with access to real applications, real interfaces, and real computing environments. Enterprises want to use AI agents for critical business workloads that touch legacy desktop applications and mainframes, yet 75% of organizations run legacy applications that lack modern APIs, and 90% of corporate data remains locked in systems never designed for agents. Agentic WorkSpaces solves this: it gives enterprises a secure, governed environment where agents and humans operate both legacy and modern applications directly, just as an employee would, without costly migrations.
Applied Scientist II, Amazon Shipping
IN, HR, Gurugram
Work on ML teams building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. As an Applied Scientist II, you will set scientific direction, mentor applied scientists, and partner with engineering and product leaders to deliver production-grade ML solutions at massive scale. Key job responsibilities 1. Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development. 2. Define and own the scientific vision and roadmap for ML solutions powering large-scale transportation planning and execution. 3. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 4. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 5. Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 6. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing. A day in the life Your day includes reviewing model performance and business metrics, guiding technical design and experimentation, mentoring scientists, and driving roadmap execution. You’ll balance near-term delivery with long-term innovation while ensuring solutions are robust, interpretable, and scalable. Ultimately, your work helps improve delivery reliability, reduce costs, and enhance the customer experience at massive scale.
Applied Scientist, Amazon Selection and Catalog Systems (ASCS)
US, NY, New York
At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research
Data Scientist, Traffic Quality
IN, KA, Bengaluru
Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience. Key job responsibilities As a Data Scientist II in Traffic Quality, you will solve inherently hard problems in advertising fraud detection by applying advanced statistical techniques and machine learning. You'll work on systems that process billions of ad impressions and clicks per day, using Amazon's cloud services including EC2, S3, EMR, Sagemaker, and RedShift. - Define and frame new research problems in fraud detection where neither problem nor solution is well-defined. - Apply new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic. - Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights. - Work with unstructured and massive datasets to deliver results. - Produce research reports meeting top-tier external publication standards. - Mentor and develop junior scientists on the team. About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf) 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf) 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)
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