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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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Applied Scientist Intern, Device and Architecture Theory, AWS Center for Quantum Computing
US, CA, Pasadena
We’re on the lookout for the curious, those who think big and want to define the world of tomorrow. At Amazon, you will grow into the high impact, visionary person you know you’re ready to be. Every day will be filled with exciting new challenges, developing new skills, and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. The Amazon Web Services (AWS) Center for Quantum Computing (CQC) in Pasadena, CA, is looking for a Quantum Research Scientist Intern in the Device and Architecture Theory group. You will be joining a multi-disciplinary team of scientists, engineers, and technicians, all working at the forefront of quantum computing to innovate for the benefit of our customers. Key job responsibilities As an intern with the Device and Architecture Theory team, you will conduct pathfinding theoretical research to inform the development of next-generation quantum processors. Potential focus areas include device physics of superconducting circuits, novel qubits and gate schemes, and physical implementations of error-correcting codes. You will work closely with both theorists and experimentalists to explore these directions. We are looking for candidates with excellent problem-solving and communication skills who are eager to work collaboratively in a team environment. Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in quantum computing and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work. A day in the life Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS. Within AWS UC, Amazon Dedicated Cloud (ADC) roles engage with AWS customers who require specialized security solutions for their cloud services. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.
Senior Data Scientist, Amazon Stores Finance Science, Amazon Stores Finance Science
US, WA, Seattle
WW Amazon Stores Finance Science (ASFS) works to leverage science and economics to drive improved financial results, foster data backed decisions, and embed science within Finance. ASFS is focused on developing products that empower controllership, improve business decisions and financial planning by understanding financial drivers, and innovate science capabilities for efficiency and scale. We are looking for a data scientist to lead high visibility initiatives for forecasting Amazon Stores' financials. You will develop new science-based forecasting methodologies and build scalable models to improve financial decision making and planning for senior leadership up to VP and SVP level. You will build new ML and statistical models from the ground up that aim to transform financial planning for Amazon Stores. We prize creative problem solvers with the ability to draw on an expansive methodological toolkit to transform financial decision-making with science. The ideal candidate combines data-science acumen with strong business judgment. You have versatile modeling skills and are comfortable owning and extracting insights from data. You are excited to learn from and alongside seasoned scientists, engineers, and business leaders. You are an excellent communicator and effectively translate technical findings into business action. Key job responsibilities Demonstrating thorough technical knowledge, effective exploratory data analysis, and model building using industry standard ML models Working with technical and non-technical stakeholders across every step of science project life cycle Collaborating with finance, product, data engineering, and software engineering teams to create production implementations for large-scale ML models Innovating by adapting new modeling techniques and procedures Presenting research results to our internal research community
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