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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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Principal Applied Scientist, FAIM (Full Funnel Agentic Intelligence and Models)
US, NY, New York
The FAIM (Full Funnel Agentic Intelligence and Models) builds the agentic AI systems behind next-generation advertising. The team develops multimodal foundation models and LLM-driven agents that power shopper understanding, audience-aware creative generation, and agentic advertising workflows. As a Principal Applied Scientist for the FAIM team, you will invent the models that help advertisers achieve desired outcomes. Today's state of the art models can setup ad campaigns to a specification, but do not understand advertising strategy well enough to help with abstract goals that real advertisers ask about like "how can I make more money." This is a rare charter to build foundational optimization science where little exists today, spanning campaign recommendation, cross-product budget allocation, incrementality measurement, and long-term-sales modeling. You will set technical direction for a growing team, partner with engineering and product to take models from research to production at Amazon scale, and directly move advertiser ROAS and new-to-brand growth. In this role you will - Own the science vision for training models in our RL Gym from end-to-end - Invent models for joint budget allocation, incrementality, and long-term value - Take innovations from prototype to production serving live advertiser campaigns - Raise the technical bar across scientists and engineers Key job responsibilities - Define the long-term scientific vision for our RL Gym based training process focusing on campaign optimization and other advertising workflows - Translating ambiguous advertiser needs and competing objectives into a concrete science roadmap. - Invent, prototype, and productionize machine learning and optimization solutions for joint budget allocation across sponsored ad products, spanning the shopper journey from awareness to purchase. - Develop rigorous approaches to incrementality measurement and long-term-sales modeling that ground optimization in true advertiser value. - Design and lead large-scale experiments and analyses to validate hypotheses and guide product direction. - Partner closely with engineering and product to define technical contracts, data schemas, and serving systems that carry models into production. - Raise the technical bar across science and engineering through mentorship, design reviews, and hands-on collaboration. - Grow scientific talent and publish impactful research internally and at top-tier venues.
Applied Scientist II, Sponsored Products Bidding
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! Within Sponsored Products, the Bidding team is responsible for defining and delivering a collection of advertising products around bid controls (dynamic bidding, bid recommendations, etc.) that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven fundamentally from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Key job responsibilities • Lead a new initiative across Sponsored Products Bidding focused on AI/ML based features. • Be the technical leader in AI, Machine Learning; lead efforts within this team and across other teams. • Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. • Drive end-to-end AI/Machine Learning projects that have a high degree of ambiguity, scale, complexity. • Build models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your AI/ML models. • Run A/B experiments, gather data, and perform statistical analysis. • Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. • Research new and innovative AI/ machine learning approaches. A day in the life Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon's Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest 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. The SPB Bidding team within Sponsored Products and Brands is focused on guiding and supporting Millions of advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in bidding systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware bidding system that leverages auction simulations, ML models, and optimization algorithms. This framework, will operate across SPB bidding system and proactively delivering value based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art bidding agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning and preference optimization), ensuring our systems are both scalable and adaptive.
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