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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.

74 Amazon Research Awards recipients announced

The awardees represent 51 universities in 17 countries. Recipients have access to more than 300 Amazon public datasets, and can utilize AWS AI/ML services and tools.

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 74 award recipients who represent 51 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.

Earlence Fernandes, Arie Gurfinkel, Hamed Haddadi, Hongxin Hu, Emmanuel Letouzé, Sanjay Rao, Bruno Ribeiro, Ramesh Sitaraman, Anirudh Sivaraman Kaushalram, Jiliang Tang, David Wagner, Xinyu Xing, and Andrew Zisserman
Top row, left to right: Earlence Fernandes, Arie Gurfinkel, Hamed Haddadi, Hongxin Hu, Emmanuel Letouzé, Sanjay Rao; second row, left to righht: Bruno Ribeiro, Ramesh Sitaraman, Anirudh Sivaraman Kaushalram, Jiliang Tang, David Wagner, Xinyu Xing; and Andrew Zisserman are among the recipients from the Amazon Research Awards Fall 2021 call for proposals under the AI for Information Security, Amazon Device Security and Privacy, Amazon Payments, Data for Social Sustainability, and Prime Video CFPs.

"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.

RecipientUniversityResearch title
Aws AlbarghouthiUniversity of Wisconsin-MadisonTeaching SMT Solvers Probability Theory
Nada AminHarvard UniversityExtensible Models and Proofs
Nora AyanianBrown UniversityLarge-Scale Labeled Multi-Agent Pathfinding for Warehouses
Clark BarrettStanford UniversityHydraScale: Solving SMT Queries in the Serverless Cloud
Ivan BeschastnikhUniversity of British ColumbiaCompiling Distributed System Models into Implementations
Nicola BezzoUniversity of VirginiaTowards Safe and Agile Robot Navigation in Occluding and Dynamic Environments
William BowmanUniversity of British ColumbiaStatic reasoning for memory in compilers and intermediate languages
Yinzhi CaoJohns Hopkins UniversityAutomatic Static Resource Analysis for Serverless Computing
Luca CarloneMassachusetts Institute of TechnologyReal-time Spatial AI for Robotics
Trevor CarlsonNational University of SingaporeAccelerating SAT Solving with a Flexible FPGA-Programming Platform
Marsha ChechikUniversity Of TorontoUnsatisfiability Proofs for Monotonic Theories
Venanzio CichellaUniversity Of IowaConcurrent allocation and planning for large-scale multi-robot systems
Cas CremersCISPA Helmholtz Center for Information SecurityKeyLife: Automated Formal Analysis for Key Lifecycles in Security Protocols with Policies, Delegation, and Compromise
Elizabeth CroftMonash UniversityHelp me!: Humans supporting robots through Augmented Reality
Jia DengPrinceton UniversityOptimization-Inspired Neural Networks for Visual SLAM
Derek DreyerMPI - SWSRefinedRust: Automating the Verification of Rust Programs in the Presence of Unsafe Code
Nima FazeliUniversity of MichiganObject Manipulation with High-Resolution Tactile Sensors
Earlence FernandesUniversity of Wisconsin-MadisonVerifiable Distributed Computation
Marcelo FriasBuenos Aires Institute of TechnologyModular Bounded Verification with Expressive Contracts
Sicun GaoUniversity of California, San DiegoInterior Search Methods in SMT
Maani Ghaffari-JadidiUniversity of MichiganRobust low-cost dead reckoning and localization for home robotics using invariant state estimation
Roberto GiacobazziUniversity of VeronaImplicit program analysis
Ronghui GuColumbia UniversityLearning Inductive Invariants for Real Distributed Protocols
Grace GuUniversity of California, BerkeleyDeep learning-enabled robust grasping for pneumatic actuators
Leonidas GuibasStanford UniversityGeneralPurpose 3D Perception of Object Functionality
Arie GurfinkelUniversity of WaterlooFormal Proofs for Trusted Execution Environments
Hamed HaddadiImperial College LondonAuditable Model Privacy using TEEs
Felix HeidePrinceton UniversityInverse Neural Rendering
Ralph HollisCarnegie Mellon UniversityLow Cost Dynamic Mobile Robots for Research and Teaching
Hongxin HuSUNY, BuffaloExplaining Learning-based Intrusion Detection Systems for Active Intrusion Responses
Jean-Baptiste JeanninUniversity of Michigan-Ann ArborAutomatic Verification of Distributed Systems Implementations
Robert KatzschmannETH ZurichDesign and Control Optimization of Soft Gripper Mechanisms for Manipulation
Anirudh Sivaraman KaushalramNew York UniversityObserving and controlling microservice deployments
Steve KoSimon Fraser UniversityPractical Symbolic Execution for Rust
Sven KoenigUniversity of Southern CaliforniaHybrid Search- and Traffic-Based MAPF Systems for Fulfillment Centers
George KonidarisBrown UniversityLearning Composable Manipulation Skills
Emmanuel LetouzéPompeu Fabra UniversityLeveraging Digital Data for Monitoring Human Rights and Social Dynamics Along and Around Value Chains
Sergey LevineUniversity of California, BerkeleyRobotic Learning with Reusable Data
Jennifer LewisHarvard UniversityComputational Co-Design of Dexterous Rigid-Soft Grippers With Intrinsic Tactile-Sensing-Based Control
Maja MatarićUniversity of Southern CaliforniaLearning User Preferences for In-Home Robots Through In Situ Augmented Reality
James NobleVictoria University Of Wellington“Programming Made Hard” Made Easier: Improving Dafny’s Human Factors
Rohan PadhyeCarnegie Mellon UniversityCoverage-Guided Property-Based Testing of Concurrent Programs
Jan PetersTU DarmstadtLearning Robot Manipulation from Tactile Feedback
Lerrel PintoNew York UniversityVisual Imitation in the Wild through Decoupled Representation Learning
Robert PlattNortheastern UniversityOn-robot manipulation learning via equivariant models
Nancy PollardCarnegie MellonContact Areas for Manipulation Capture, Retargeting, and Hand Design
Pavithra PrabhakarKansas State UniversityConformance Checking of Evolving ML Software Systems
Francesco RanzatoUniversity of VeronaImplicit program analysis
Sanjay RaoPurdue UniversityAnswering counterfactuals from offline data for video streaming
Bruno RibeiroPurdue UniversityAnswering counterfactuals from offline data for video streaming
Talia RingerUniversity of Illinois Urbana-ChampaignNeurosymbolic Proof Synthesis & Repair
Alessandro RizzoPolitecnico di TorinoPhysics-Informed Machine Learning for Trustworthy Control of Autonomous Robots
Camilo RochaPontificia Universidad Javeriana CaliProbabilistic and Symbolic Tools for P Program Verification
Andrei SabelfeldChalmers University of TechnologyDeepCrawl: Automated Reasoning for Deep Web Crawling
Oren SalzmanTechnion - Israel Institute of TechnologyIncreasing throughput in automated warehouses via environment manipulation
Ilya SergeyNational University of SingaporeScaling Automated Verification of Distributed Protocols with Specification Transformation and Synthesis
Michele SevegnaniUniversity of GlasgowFrom Whiteboards to Models: Diagrammatic Formal Modelling for Everyone
Roland SiegwartETH ZurichAutonomous Navigation of Aerial Robotic Manipulators in Unstructured Indoor and Outdoor Environments
Ramesh SitaramanUniversity of Massachusetts AmherstDesign and Evaluation of ABR Algorithms for High-Performance Video Delivery
Fu SongShanghaiTech UniversityEfficient and Precise Verification for Constant-Time and Time-Balancing of Cryptosystems
Zhendong SuETH ZurichPractical Techniques for Reliable, Robust and Performant SMT Solvers
Jiliang TangMichigan State UniversityTaming Graph Anomaly Detection via Graph Neural Networks
Pratap TokekarUniversity of Maryland, College ParkMulti-Robot Coordination through the Lens of Risk
Daniel VarroMcGill UniversityGraph Solver as a Service
Yakir VizelTechnion - Israel Institute of TechnologyQuantified Invariants
David WagnerUniversity of California, BerkeleyMachine Learning for Malware Detection: Robustness against Concept Drift
James WangPennsylvania State UniversityAffective and Social Interaction between Human and Intelligent Machine in Daily Activities
Shenlong WangUniversity of Illinois Urbana-ChampaignSafely Test Autonomous Vehicles with Augmented Reality
Thomas WiesNew York UniversityA Modular Library of Verified Concurrent Search Structure Algorithms
Anton WijsEindhoven University of TechnologyMany-Core Acceleration of State Space Construction and Analysis
Xinyu XingNorthwestern UniversityBattling Noisy-label Classification
Meng XuUniversity Of WaterlooFinding Specification Blind Spots with Fuzz Testing
Yuke ZhuUniversity of Texas at AustinInteractive Learning Framework for Building Structured Object Models from Play
Andrew ZissermanUniversity of OxfordAudio-Visual Synchronisation for General Videos

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Job summaryAmazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. 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!The Advertising Forecasting Science team comprises top scientists and engineers based in Palo Alto, California. The team builds forecasting models for advertising campaigns and financial planning, with revenue exceeding tens of billions of dollars. The forecasting science team makes auction prediction and handles bid optimization for billions of daily requests using innovative machine learning algorithms to optimize performance, which generates billions of annual revenue!As an Applied Scientist on this team, you will: Develop scalable and effective machine Learning models with automated training, validation, monitoring and reporting.Work with talented scientists and engineers to solve problems in the domains of forecasting, auction theory, bid optimization, and user clustering.Conduct deep data analyses on massive ad user and contextual data sets.Invent ways to overcome technical limitations and enable new forms of analyses to drive key technical and business decisions.Stay familiar with the field and apply state-of-the-art machine learning techniques to our domain problems, around forecasting, bidding, allocation, and optimization.Produce peer-reviewed scientific paper in top journals and conferences.Present results, reports, and data insights to both technical and business leadership.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.Team video https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Job summaryDo you want to have a worldwide impact in Robotics? The Robotics AI team at Amazon builds high-performance, real-time robotic systems that can perceive, learn, and act intelligently alongside humans—at Amazon scale. We invent and scale AI systems for robotics in fulfillment. Our mission is to enable robots to interact safely, efficiently, and fluently with the clutter and uncertainty of real-world fulfillment centers. We hire and develop subject matter experts in robotics with a focus on computer vision, deep learning, intelligent control, semi-supervised and unsupervised learning. We are seeking hands-on, Applied Science Manager to own the development of Perception and Task planning algorithms to advance robotics in our fulfillment network along with leading teams. You will be deep in algorithms and code. A successful candidate would be an experienced people manager with good leadership skills combined with excellent technical depth in Computer vision/ Deep Learning/ Perception systems/ Task planning, great communication skills, and a drive to achieve results in a collaborative team environment. In this role you will provide people management and also apply the latest trends in research to solve real-world problems. You will be an integral part of the core robotics team and work with others to implement robotics systems above and beyond the current state-of-the-art in the field.You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a fearless disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers.Key job responsibilities* As a manager, you will be responsible for delivering and maintaining critical robotic capabilities in the fulfillment network.* You will drive your team to research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines* You will prioritize being a great people manager: motivating, rewarding, and coaching your diverse team is the most important part of this role. You will recruit and retain top talent and excel in day-to-day people and performance management tasks.* You will keep your technical skills current to contribute to architecture and design discussions. * You will regularly take part in deep-dive exercises and drive technical post-mortem discussions to identify the root cause of complex issues.* Set a vision for your team and create product roadmaps. Help your team sort out technical and product requirements and priorities. Use project management skills to deliver product roadmap items and other cross-team projects.