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Amazon today publicly announced 74 recipients from the Amazon Research Awards Fall 2021 call for proposals. The recipients, who represent 51 universities in 17 countries, have access to more than 300 Amazon public datasets, and can utilize AWS AI/ML services and tools.

75 Amazon Research Awards recipients announced

The awardees represent 52 universities in 17 countries. Recipients have access to more than 300 Amazon public datasets, and can utilize AWS AI/ML services and tools.

By Amazon Research Awards team
July 18, 2022
8 min read
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The Amazon Research Awards is 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 75 award recipients who represent 52 universities in 17 countries. 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.

Top row, left to right: Aws Albarghouthi, Nada Amin, Clark Barrett, Ivan Beschastnikh, William Bowman, Yinzhi Cao, Trevor Carlson, Marsha Chechik; second row, left to right: Cas Cremers, Derek Dreyer, Marcelo Frias, Sicun Gao, Roberto Giacobazzi, Ronghui Gu, Jean-Baptiste Jeannin, Steve Ko; third row, left to right: James Noble, Rohan Padhye, Pavithra Prabhakar, Francesco Ranzato, Talia Ringer, Camilo Rocha, Andrei Sabelfeld, Ilya Sergey; and bottom row, left to right: Michele Sevegnani, Fu Song, Zhendong Su, Daniel Varro, Yakir Vizel, Thomas Wies, Anton Wijs, and Meng Xu.
Top row, left to right: Aws Albarghouthi, Nada Amin, Clark Barrett, Ivan Beschastnikh, William Bowman, Yinzhi Cao, Trevor Carlson, Marsha Chechik; second row, left to right: Cas Cremers, Derek Dreyer, Marcelo Frias, Sicun Gao, Roberto Giacobazzi, Ronghui Gu, Jean-Baptiste Jeannin, Steve Ko; third row, left to right: James Noble, Rohan Padhye, Pavithra Prabhakar, Francesco Ranzato, Talia Ringer, Camilo Rocha, Andrei Sabelfeld, Ilya Sergey; and bottom row, left to right: Michele Sevegnani, Fu Song, Zhendong Su, Daniel Varro, Yakir Vizel, Thomas Wies, Anton Wijs, and Meng Xu are among the recipients from the Amazon Research Awards Fall 2021 call for proposals under the Automated Reasoning CFP.

This announcement includes awards funded under seven call for proposals during the Fall 2021 cycle: AI for Information Security, Amazon Device Security and Privacy, Amazon Payments, AWS Automated Reasoning, Data for Social Sustainability, Prime Video, 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.

Top row, left to right: Nora Ayanian, Nicola Bezzo, Luca Carlone, Venanzio Cichella, Jia Deng, Nima Fazeli, Maani Ghaffari-Jadidi; second row, left to right: Grace Gu, Leonidas Guibas, Felix Heide, Ralph Hollis, Robert Katzschmann, Sven Koenig, George Konidaris; third row, left to right: Sergey Levine, Jennifer Lewis, Maja Matarić, Jan Peters, Lerrel Pinto, Robert Platt, Nancy Pollard; and bottom row, left to right: Alessandro Rizzo, Oren Salzman, Roland Siegwart, Pratap Tokekar, James Wang, Shenlong Wang, and Yuke Zhu.
Top row, left to right: Nora Ayanian, Nicola Bezzo, Luca Carlone, Venanzio Cichella, Jia Deng, Nima Fazeli, Maani Ghaffari-Jadidi; second row, left to right: Grace Gu, Leonidas Guibas, Felix Heide, Ralph Hollis, Robert Katzschmann, Sven Koenig, George Konidaris; third row, left to right: Sergey Levine, Jennifer Lewis, Maja Matarić, Jan Peters, Lerrel Pinto, Robert Platt, Nancy Pollard; and bottom row, left to right: Alessandro Rizzo, Oren Salzman, Roland Siegwart, Pratap Tokekar, James Wang, Shenlong Wang, and Yuke Zhu are among the recipients from the Amazon Research Awards Fall 2021 call for proposals under the Robotics CFP.

Recipients have access to more than 300 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.

Top row, left to right:NAMES; second row, left to right:NAMES are among the recipients from the Amazon Research Awards Winter 2022 call for proposals under the Alexa: Fairness in AI CFP.
Top row, left to right:NAMES; second row, left to right:NAMES are among the recipients from the Amazon Research Awards Winter 2022 call for proposals under the Alexa: Fairness in AI CFP.

"Research in automated reasoning is deeply intertwined with a broad range of other research areas, touching machine learning, hardware and software engineering, robotics, and life sciences," said Daniel Kroening, an Automated Reasoning Group senior principal scientist. "The 2021 Amazon Research Awards reflect this breadth, and the interdisciplinary nature of research that is necessary to take computing one step closer to that magic spark that drives human reasoning."

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.

The table below lists, in alphabetical order, Fall 2021 cycle call-for-proposal recipients.

Recipient

University

Research title

Aws Albarghouthi

University of Wisconsin-Madison

Teaching SMT Solvers Probability Theory

Nada Amin

Harvard University

Extensible Models and Proofs

Nora Ayanian

Brown University

Large-Scale Labeled Multi-Agent Pathfinding for Warehouses

Clark Barrett

Stanford University

HydraScale: Solving SMT Queries in the Serverless Cloud

Ivan Beschastnikh

University of British Columbia

Compiling Distributed System Models into Implementations

Nicola Bezzo

University of Virginia

Towards Safe and Agile Robot Navigation in Occluding and Dynamic Environments

William Bowman

University of British Columbia

Static reasoning for memory in compilers and intermediate languages

Yinzhi Cao

Johns Hopkins University

Automatic Static Resource Analysis for Serverless Computing

Luca Carlone

Massachusetts Institute of Technology

Real-time Spatial AI for Robotics

Trevor Carlson

National University of Singapore

Accelerating SAT Solving with a Flexible FPGA-Programming Platform

Marsha Chechik

University Of Toronto

Unsatisfiability Proofs for Monotonic Theories

Venanzio Cichella

University Of Iowa

Concurrent allocation and planning for large-scale multi-robot systems

Cas Cremers

CISPA Helmholtz Center for Information Security

KeyLife: Automated Formal Analysis for Key Lifecycles in Security Protocols with Policies, Delegation, and Compromise

Elizabeth Croft

Monash University

Help me!: Humans supporting robots through Augmented Reality

Jia Deng

Princeton University

Optimization-Inspired Neural Networks for Visual SLAM

Derek Dreyer

MPI - SWS

RefinedRust: Automating the Verification of Rust Programs in the Presence of Unsafe Code

Tudor Dumitras

University of Maryland, College Park

Mitigating the impact of behavior variability and label noise on ML-based malware detectors

Nima Fazeli

University of Michigan

Object Manipulation with High-Resolution Tactile Sensors

Earlence Fernandes

University of Wisconsin-Madison

Verifiable Distributed Computation

Marcelo Frias

Buenos Aires Institute of Technology

Modular Bounded Verification with Expressive Contracts

Sicun Gao

University of California, San Diego

Interior Search Methods in SMT

Maani Ghaffari-Jadidi

University of Michigan

Robust low-cost dead reckoning and localization for home robotics using invariant state estimation

Roberto Giacobazzi

University of Verona

Implicit program analysis

Ronghui Gu

Columbia University

Learning Inductive Invariants for Real Distributed Protocols

Grace Gu

University of California, Berkeley

Deep learning-enabled robust grasping for pneumatic actuators

Leonidas Guibas

Stanford University

GeneralPurpose 3D Perception of Object Functionality

Arie Gurfinkel

University of Waterloo

Formal Proofs for Trusted Execution Environments

Hamed Haddadi

Imperial College London

Auditable Model Privacy using TEEs

Felix Heide

Princeton University

Inverse Neural Rendering

Ralph Hollis

Carnegie Mellon University

Low Cost Dynamic Mobile Robots for Research and Teaching

Hongxin Hu

SUNY, Buffalo

Explaining Learning-based Intrusion Detection Systems for Active Intrusion Responses

Jean-Baptiste Jeannin

University of Michigan-Ann Arbor

Automatic Verification of Distributed Systems Implementations

Robert Katzschmann

ETH Zurich

Design and Control Optimization of Soft Gripper Mechanisms for Manipulation

Anirudh Sivaraman Kaushalram

New York University

Observing and controlling microservice deployments

Steve Ko

Simon Fraser University

Practical Symbolic Execution for Rust

Sven Koenig

University of Southern California

Hybrid Search- and Traffic-Based MAPF Systems for Fulfillment Centers

George Konidaris

Brown University

Learning Composable Manipulation Skills

Emmanuel Letouzé

Pompeu Fabra University

Leveraging Digital Data for Monitoring Human Rights and Social Dynamics Along and Around Value Chains

Sergey Levine

University of California, Berkeley

Robotic Learning with Reusable Data

Jennifer Lewis

Harvard University

Computational Co-Design of Dexterous Rigid-Soft Grippers With Intrinsic Tactile-Sensing-Based Control

Maja Matarić

University of Southern California

Learning User Preferences for In-Home Robots Through In Situ Augmented Reality

James Noble

Victoria University Of Wellington

“Programming Made Hard” Made Easier: Improving Dafny’s Human Factors

Rohan Padhye

Carnegie Mellon University

Coverage-Guided Property-Based Testing of Concurrent Programs

Jan Peters

TU Darmstadt

Learning Robot Manipulation from Tactile Feedback

Lerrel Pinto

New York University

Visual Imitation in the Wild through Decoupled Representation Learning

Robert Platt

Northeastern University

On-robot manipulation learning via equivariant models

Nancy Pollard

Carnegie Mellon

Contact Areas for Manipulation Capture, Retargeting, and Hand Design

Pavithra Prabhakar

Kansas State University

Conformance Checking of Evolving ML Software Systems

Francesco Ranzato

University of Verona

Implicit program analysis

Sanjay Rao

Purdue University

Answering counterfactuals from offline data for video streaming

Bruno Ribeiro

Purdue University

Answering counterfactuals from offline data for video streaming

Talia Ringer

University of Illinois Urbana-Champaign

Neurosymbolic Proof Synthesis & Repair

Alessandro Rizzo

Politecnico di Torino

Physics-Informed Machine Learning for Trustworthy Control of Autonomous Robots

Camilo Rocha

Pontificia Universidad Javeriana Cali

Probabilistic and Symbolic Tools for P Program Verification

Andrei Sabelfeld

Chalmers University of Technology

DeepCrawl: Automated Reasoning for Deep Web Crawling

Oren Salzman

Technion - Israel Institute of Technology

Increasing throughput in automated warehouses via environment manipulation

Ilya Sergey

National University of Singapore

Scaling Automated Verification of Distributed Protocols with Specification Transformation and Synthesis

Michele Sevegnani

University of Glasgow

From Whiteboards to Models: Diagrammatic Formal Modelling for Everyone

Roland Siegwart

ETH Zurich

Autonomous Navigation of Aerial Robotic Manipulators in Unstructured Indoor and Outdoor Environments

Ramesh Sitaraman

University of Massachusetts Amherst

Design and Evaluation of ABR Algorithms for High-Performance Video Delivery

Fu Song

ShanghaiTech University

Efficient and Precise Verification for Constant-Time and Time-Balancing of Cryptosystems

Zhendong Su

ETH Zurich

Practical Techniques for Reliable, Robust and Performant SMT Solvers

Jiliang Tang

Michigan State University

Taming Graph Anomaly Detection via Graph Neural Networks

Pratap Tokekar

University of Maryland, College Park

Multi-Robot Coordination through the Lens of Risk

Daniel Varro

McGill University

Graph Solver as a Service

Yakir Vizel

Technion - Israel Institute of Technology

Quantified Invariants

David Wagner

University of California, Berkeley

Machine Learning for Malware Detection: Robustness against Concept Drift

James Wang

Pennsylvania State University

Affective and Social Interaction between Human and Intelligent Machine in Daily Activities

Shenlong Wang

University of Illinois Urbana-Champaign

Safely Test Autonomous Vehicles with Augmented Reality

Thomas Wies

New York University

A Modular Library of Verified Concurrent Search Structure Algorithms

Anton Wijs

Eindhoven University of Technology

Many-Core Acceleration of State Space Construction and Analysis

Xinyu Xing

Northwestern University

Battling Noisy-label Classification

Meng Xu

University Of Waterloo

Finding Specification Blind Spots with Fuzz Testing

Yuke Zhu

University of Texas at Austin

Interactive Learning Framework for Building Structured Object Models from Play

Andrew Zisserman

University of Oxford

Audio-Visual Synchronisation for General Videos
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Data Scientist II, Device Economics
US, CA, Sunnyvale
We are seeking Data Scientist II with strong science application skills to join our Device Economics team. This role will focus primarily on Amazon's innovative devices and services (e.g. Echo Family of Devices), working at the intersection of economic modeling, forecasting science, and business strategy. The ideal candidate will be responsible for pre-launch forecasts, annualized overall forecasts, identifying substitution patterns, and partnering closely with product managers and marketing managers to understand the evolution of the Devices portfolio. Key job responsibilities Forecasting & Modeling 1. Develop and maintain pre-launch forecasts and annualized overall forecasts for Amazon Devices 2. Identify and model substitution patterns across the device portfolio 3. Build economic and financial models to support demand planning and business decisions 4. Formulate relevant analytical frameworks to address key economic issues in device forecasting Science Communication & Collaboration 1. Explain complex science models and methodologies to non-technical stakeholders including product managers and marketing managers 2. Collaborate with economists, data scientists, and applied scientists across Decision Science 3. Present results of analyses to cross-functional teams and leadership 4. Build trust in science models and forecast outputs with product teams Innovation & Strategic Thinking 1. Think creatively about ways that leading-edge analytics and emerging data sources can address Devices' most pressing business challenges 2. Help internal teams leverage analytic tools to better manage innovation 3. Conduct empirical studies and perform quantitative and qualitative research 4. Identify opportunities to improve forecasting accuracy and business impact Cross-Functional Partnership 1. Work closely with product managers and marketing managers to understand portfolio evolution and business strategy 2. Support DSO leadership in quarterly business reviews and strategic planning A day in the life Your days will be split between refining and building models and working with business leaders to interpret them. You own science-based forecasts that can directly impact Amazon's bottom line on the order of multi-million dollar decisions. - You will perform model refreshes or updates to analyses as needed; and, - You will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems. About the team The Decision Science team within DSO (Device Supply Organization) is responsible for forecasting and demand planning initiatives across Amazon Devices. The DSO team of 300+ engineers, scientists, and PMs applies quantitative methods and data-driven approaches to replace judgment-based decisions with science-driven forecasts. Decision Science focuses on lifetime demand forecasting using econometric and machine learning models for rapid reforecasting, mix adjustments, and portfolio management for new product launches. We also inform to go/no-go investment decision for new product initiatives
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