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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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Applied Scientist II, Sponsored Products and Brands
US, WA, Seattle
About Sponsored Products and Brands The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About our team The Gnome team within the Sponsored Products and Brands (SPB) improves ad selection helping shoppers reach their shopping mission. To do this, we apply a broad range of machine learning, causal inference, reinforcement learning based optimization techniques and LLMs to continuously explore, learn, and optimize ads shown. We are an interdisciplinary team with a focus on customer obsession and inventing and simplifying. Our primary focus is on improving the ads experience by gaining a deep understanding of shopper pain points and developing new innovative solutions to address them. A day in the life As an Applied Scientist on this team, you will be responsible to improve quality of ads shown using in-session and offline signals via online experimentation, ML modeling, simulation, and online feedback. As an Applied Scientist on this team, you will identify opportunities for the team to make a direct impact on customers and the search experience. You will work closely with with search and retail partner teams, software engineers and product managers to build scalable real-time ML solutions. You will have the opportunity to design, run, and analyze A/B experiments that improve the experience of millions of Amazon shoppers while driving quantifiable revenue impact while broadening your technical skillset. #GenAI
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