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2020 Amazon Research Awards recipients announced

ARA funds nearly twice as many awards as in previous year; 100 award recipients represent 59 universities in 13 countries.

By Amazon Research Awards team
April 27, 2021
11 min read
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In March 2021, Amazon notified applicants that they were recipients of the 2020 Amazon Research Awards, a program that provides unrestricted funds and AWS Promotional Credits to academic researchers investigating research topics across a number of disciplines.

Today, we’re publicly announcing the 100 award recipients who represent 59 universities in 13 countries. This round, ARA received a record number of submissions and funded nearly twice as many awards as the previous year. Each award 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.

ARA is funding awards under five call for proposals: AI for Information Security, Alexa Fairness in AI, AWS AI, AWS Automated Reasoning, and Robotics. Proposals were reviewed for the quality of their scientific content, their creativity, and their potential to impact both the research community, and society more generally. Theoretical advances, creative new ideas, and practical applications were all considered.

Recipients have access to more than 200 Amazon public datasets, and can utilize AWS AI/ML services and tools through their AWS Promotional Credits. Recipients also are assigned an Amazon research contact who offers consultation and advice along with opportunities to participate in Amazon events and training sessions.

Additionally, Amazon encourages the publication of research results, presentations of research at Amazon offices worldwide, and the release of related code under open-source licenses.

“The 2020 Amazon Research Awards recipients represent a distinguished array of academic researchers who are pursuing research across areas such as ML algorithms and theory, fairness in AI, computer vision, natural language processing, edge computing, and medical research,” said Bratin Saha, vice president of AWS Machine Learning Services. “We are excited by the depth and breadth of their proposals, as well as the opportunity to advance the science through strengthened connections among academic researchers, their institutions, and our research teams.”

“As we enter into this golden age of robotics, we do so with our university partners. Not only are they shaping what is possible in robotics, they are inspiring many next- generation roboticists with their incredible creations and front-line teachings,” said Tye Brady, chief technologist for Amazon Robotics. “Our grant recipients are not only pursuing cutting-edge research that will benefit society, but perhaps more importantly are helping students from across the globe pursue a career in science and engineering.”

ARA funds proposals up to four times a year in a variety of research areas. Applicants are encouraged to visit the ARA call for proposals page for more information or send an email to be notified of future open calls.

Below is the list of 2020 award recipients, presented in alphabetical order.

Recipient

University

Research title

Vikram Adve

University of Illinois Urbana-Champaign

Extending the LLVM compiler infrastructure for tensor architectures

Pulkit Agrawal

Massachusetts Institute of Technology

A framework for multi-step planning for manipulating rigid objects

Ron Alterovitz

University of North Carolina at Chapel Hill

Cloud-based motion planning: an enabling technology for next-generation autonomous robots

Jimmy Ba

University of Toronto

Model-based reinforcement learning with causal world models

Saurabh Bagchi

Purdue University—West Lafayette

Content and contention-aware approximate streaming video analytics for edge devices

David Baker Effendi

Stellenbosch University

Dataflow analysis using code property graphs, graph databases and synchronized pushdown systems

Sivaraman Balakrishnan

Carnegie Mellon University

Foundations of robust machine learning: from principled approaches to practice

Elias Bareinboim

Columbia University

Off-policy evaluation through causal modeling

Clark Barrett

Stanford University

Model-based testing of SMT solvers

Lars Birkedal

Aarhus University

Modular reasoning about distributed systems: higher-order distributed separation logic

David Blei

Columbia University

New directions in observational causal inference

Eric Bodden

Paderborn University

HybridCG — dynamically-enriched call-Graph generation of Java enterprise applications

Legand Burge

Howard University

Voice-FAQ: artificial intelligence for triaging cognitive decline through modeling vocal prosody and facial expressions

James Caverlee

Texas A&M University, College Station

Fairness in recommendation without demographics

Changyou Chen

University at Buffalo

Scaling up human-action analysis systems

Danqi Chen

Princeton University

Building broad-coverage, structured dense knowledge bases for natural language processing tasks

Helen Chen

University of Waterloo

Optimizing pretrained clinical embeddings for automatic COVID-related ICD coding

Yiran Chen

Duke University

Privacy-preserving representation learning on graphs — a mutual information perspective

Margarita Chli

ETH Zurich

Vision-based emergency landing in urban environments using reinforcement learning and deep learning

Kyunghyun Cho

New York University

Independently controllable attributes for controllable neural text generation

Carlo Ciliberto

University College London

Optimal transport for meta-learning

Loris D’Antoni

University of Wisconsin–Madison

Correct-by-construction IAM policies

David Danks

Carnegie Mellon University

An integrated framework for understanding human-AI hybrid decision-making

Suhas Diggavi

University of California, Los Angeles

Compressed private and secure distributed edge learning

Greg Durrett

University of Texas At Austin

Making conditional text generation fair and factual

Sergio Escalera

Universitat de Barcelona and Computer Vision Center

Portable virtual try-on for smart devices

Jan Faigl

Czech Technical University in Prague

Communication maps building in subterranean environments

Pietro Ferrara

Ca’ Foscari University of Venice

IAM access control policies verification and inference

Katerina Fragkiadaki

Carnegie Mellon University

Generalizing manipulation across objects, configurations and views using a visually-grounded library of behaviors

Guillermo Gallego

Technical University of Berlin

Online in-hand object tracking and grasp failure detection with an event-based camera

Grace Gao

Stanford University

Trustworthy autonomous vehicle localization using a joint model-driven and data-driven approach

Stephanie Gil

Harvard University

Enabling the next generation of coordinated robots: scalable real-time decision making

Luca Giuggioli

University of Bristol

Multi-robot online exploration in extreme unbounded environments through adaptive socio-spatial ordering

Jorge Goncalves

University of Melbourne

Integrated qualification test framework to measure crowd worker quality and assign or recommend heterogeneous tasks

Ananth Grama

Purdue University—West Lafayette

Scaling causal inference to explainable clinical recommendations

Grace Gu

University of California, Berkeley

Surrogate machine learning model and quasi-static simulation of pneumatically actuated robotic devices

Ronghui Gu

Columbia University

Microverification of the Linux KVM hypervisor: proving VM confidentiality and integrity

Aarti Gupta

Princeton University

Learning abstract specifications from distributed program implementations

Saurabh Gupta

University of Illinois Urbana-Champaign

Self-supervised discovery of object states and transitions from unlabeled videos

Daniel Harabor

Monash University

Anytime constraint-based multi-agent pathfinding

Hynek Hermansky

Johns Hopkins University

Multistream lifelong federated learning for machine recognition of speech

Bin Hu

University of Illinois Urbana-Champaign

Provably robust adversarial reinforcement learning for sequential decision making in safety-critical environments

Lifu Huang

Virginia Tech

Event-centric temporal and causal knowledge acquisition and generalization for natural language understanding

Dinesh Jayaraman

University of Pennsylvania

Learning modular dynamics models for plug-and-play visual control

Sven Koenig

University of Southern California

Improving planning and plan execution for warehouse automation

Laura Kovacs

TU Wien

FOREST: first-order reasoning for ensuring system security

Arun Kumar

University of California, San Diego

Improving automated feature type inference for AutoML on tabular data

Himabindu Lakkaraju

Harvard University

Towards reliable and robust model explanations

Kevin Leyton-Brown

University of British Columbia

Automated machine learning for tabular datasets using hyperband embedded reinforcement learning

Bo Li

University of Illinois Urbana-Champaign

Machine learning evaluation as a service for robustness, fairness, and privacy utilities

Ke Li

University of Exeter

Many hands make work light: multi-task deep semantic learning for testing web application firewalls

Zhiqiang Lin

Ohio State University

Type-aware recovery of symbol names in binary code: a machine learning based approach

Jeffrey Liu

Massachusetts Institute of Technology

Integrating the low altitude disaster imagery (LADI) dataset into the MIT Beaver Works curriculum

Michael Mahoney

University of California, Berkeley

Systematic methods for efficient inference and training of neural networks

Radu Marculescu

University of Texas

New directions for 3D object detection: distributed inference on edge devices using knowledge distillation

Ruben Martins

Carnegie Mellon University

Improving performance and trust of MaxSAT solvers

Jiri Matas

Czech Technical University in Prague

Training neural networks on non-differentiable losses

Michael Milford

Queensland University of Technology

Complementarity-aware multi-process fusion for long term localization

Heather Miller

Carnegie Mellon University

Directed automated explicit-state model checking for distributed applications

Ndapa Nakashole

University of California, San Diego

Learning representations for voice-based conversational agents for older adults

Shrikanth Narayanan

University of Southern California

Toward inclusive human-AI conversational experiences for children

Lerrel Pinto

New York University

Learning to manipulate deformable objects through robust simulations

Ravi Ramamoorthi

University of California, San Diego

Sparse multi-view object acquisition using learned volumetric representations

Philip Resnik

University of Maryland, College Park

Advanced topic modeling to support the understanding of COVID-19 and its effects

Daniela Rus

Massachusetts Institute of Technology

Learning to plan through imagined self-play for multi-agent system

Supreeth Shashikumar

University of California, San Diego

Privacy preserving continual learning with applications to critical care

Robert Shepherd

Cornell University

Enduring and adaptive robots via electrochemical blood

Cong Shi

University of Michigan, Ann Arbor

Machine learning for personalized assortment optimization

Florian Shkurti

University of Toronto

Generating physically realizable adversarial driving scenarios via differentiable physics and rendering simulators

Abhinav Shrivastava

University of Maryland, College Park

The pursuit of knowledge: discovering and localizing new concepts using dual memory

Roland Siegwart

ETH Zurich

Safe self-calibration of hybrid aerial vehicles

Sameer Singh

University of California, Irvine

Detecting and fixing vulnerabilities in NLP models via semantic perturbations and tracing data influence

Noah Smith

University of Washington - Seattle

Language model customization

Mahdi Soltanolkotabi

University of Southern California

Artificial intelligence for fast and portable medical imaging (with limited training data)

Seung Woo Son

University of Massachusetts Lowell

Reliable and accurate anomaly detection in edge nodes using sparsity profile

Dawn Song

University of California, Berkeley

Knowledge-enhanced cyber threat hunting

Dezhen Song

Texas A&M University, College Station

Optoacoustic material and structure pretouch sensing at robot fingertip

Shuran Song

Columbia University

Dexterity through diversity:learning a generalizable grasping policy for diverse end-effectors

Yizhou Sun

University of California, Los Angeles

Accelerating graph neural network training

Russ Tedrake

Massachusetts Institute of Technology

Intuitive physics for manipulation

James Tompkin

Brown University

Real-time multi-camera fusion for unoccluded VR robot teleoperation

Emina Torlak

University of Washington - Seattle

Automated verification of JIT compilers for BPF

Marynel Vazquez

Yale University

Evaluating social robot navigation via online human-driven simulations

Nisheeth Vishnoi

Yale University

Fair and error-resilient algorithms for AI and ML

Gang Wang

University of Illinois at Urbana–Champaign

Combating concept drift in security applications via proactive data synthesis

Hao Wang

Rutgers University-New Brunswick

Structured domain adaptation with applications to personalization and forecasting

James Wang

Pennsylvania State University

Affective and social interaction between human and intelligent machine

Gloria Washington

Howard University

Towards identification of uncomfortable speech in conversations

Chuan Wu

The University of Hong Kong

Compilation optimization in distributed DNN training: joining OP and tensor fusion/partition

Eugene Wu

Columbia University

Human-in-the-loop data debugging for ML-oriented analytics

Jiajun Wu

Stanford University

Implicit dynamic scene representation learning for robotics

Ming-Ru Wu

Dana-Farber Cancer Institute

From bench to clinic – machine-learning based cancer immunotherapy design

Diyi Yang

Georgia Institute of Technology

Abstractive conversation summarization at scale

Sixian You

Massachusetts Institute of Technology

AI-driven label-free histology for cancer diagnosis

Jingjin Yu

Rutgers University-New Brunswick

Pushing the limits of efficient and optimal multi-agent path finding through exploring space utilization optimization and adaptive planning horizon heuristics

Rui Zhang

Pennsylvania State University

Building robust conversational question answering systems over databases of tabular data

Yu Zhang

University of South Florida

Design of an automated advanced air mobility flight planning system (AAFPS)

Yuke Zhu

University of Texas at Austin

Learning implicit shape affordance for grasping and manipulation

Marinka Zitnik

Harvard University

Actionable graph learning for finding cures for emerging diseases

James Zou

Stanford University

How to make AI forget you? Efficiently removing individuals’ data from machine learning models
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Member of Technical Staff, Frontier AI & Robotics (FAR)
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As a Member of Technical Staff, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
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