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.

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.

RecipientUniversityResearch title
Vikram AdveUniversity of Illinois Urbana-ChampaignExtending the LLVM compiler infrastructure for tensor architectures
Pulkit AgrawalMassachusetts Institute of TechnologyA framework for multi-step planning for manipulating rigid objects
Ron AlterovitzUniversity of North Carolina at Chapel HillCloud-based motion planning: an enabling technology for next-generation autonomous robots
Jimmy BaUniversity of TorontoModel-based reinforcement learning with causal world models
Saurabh BagchiPurdue University—West LafayetteContent and contention-aware approximate streaming video analytics for edge devices
David Baker EffendiStellenbosch UniversityDataflow analysis using code property graphs, graph databases and synchronized pushdown systems
Sivaraman BalakrishnanCarnegie Mellon UniversityFoundations of robust machine learning: from principled approaches to practice
Elias BareinboimColumbia UniversityOff-policy evaluation through causal modeling
Clark BarrettStanford UniversityModel-based testing of SMT solvers
Lars BirkedalAarhus UniversityModular reasoning about distributed systems: higher-order distributed separation logic
David BleiColumbia UniversityNew directions in observational causal inference
Eric BoddenPaderborn UniversityHybridCG — dynamically-enriched call-Graph generation of Java enterprise applications
Legand BurgeHoward UniversityVoice-FAQ: artificial intelligence for triaging cognitive decline through modeling vocal prosody and facial expressions
James CaverleeTexas A&M University, College StationFairness in recommendation without demographics
Changyou ChenUniversity at BuffaloScaling up human-action analysis systems
Danqi ChenPrinceton UniversityBuilding broad-coverage, structured dense knowledge bases for natural language processing tasks
Helen ChenUniversity of WaterlooOptimizing pretrained clinical embeddings for automatic COVID-related ICD coding
Yiran ChenDuke UniversityPrivacy-preserving representation learning on graphs — a mutual information perspective
Margarita ChliETH ZurichVision-based emergency landing in urban environments using reinforcement learning and deep learning
Kyunghyun ChoNew York UniversityIndependently controllable attributes for controllable neural text generation
Carlo CilibertoUniversity College LondonOptimal transport for meta-learning
Loris D'AntoniUniversity of Wisconsin–MadisonCorrect-by-construction IAM policies
David DanksCarnegie Mellon UniversityAn integrated framework for understanding human-AI hybrid decision-making
Suhas DiggaviUniversity of California, Los AngelesCompressed private and secure distributed edge learning
Greg DurrettUniversity of Texas At AustinMaking conditional text generation fair and factual
Sergio EscaleraUniversitat de Barcelona and Computer Vision CenterPortable virtual try-on for smart devices
Jan FaiglCzech Technical University in PragueCommunication maps building in subterranean environments
Pietro FerraraCa' Foscari University of VeniceIAM access control policies verification and inference
Katerina FragkiadakiCarnegie Mellon UniversityGeneralizing manipulation across objects, configurations and views using a visually-grounded library of behaviors
Guillermo GallegoTechnical University of BerlinOnline in-hand object tracking and grasp failure detection with an event-based camera
Grace GaoStanford UniversityTrustworthy autonomous vehicle localization using a joint model-driven and data-driven approach
Stephanie GilHarvard UniversityEnabling the next generation of coordinated robots: scalable real-time decision making
Luca GiuggioliUniversity of BristolMulti-robot online exploration in extreme unbounded environments through adaptive socio-spatial ordering
Jorge GoncalvesUniversity of MelbourneIntegrated qualification test framework to measure crowd worker quality and assign or recommend heterogeneous tasks
Ananth GramaPurdue University—West LafayetteScaling causal inference to explainable clinical recommendations
Grace GuUniversity of California, BerkeleySurrogate machine learning model and quasi-static simulation of pneumatically actuated robotic devices
Ronghui GuColumbia UniversityMicroverification of the Linux KVM hypervisor: proving VM confidentiality and integrity
Aarti GuptaPrinceton UniversityLearning abstract specifications from distributed program implementations
Saurabh GuptaUniversity of Illinois Urbana-ChampaignSelf-supervised discovery of object states and transitions from unlabeled videos
Daniel HaraborMonash UniversityAnytime constraint-based multi-agent pathfinding
Hynek HermanskyJohns Hopkins UniversityMultistream lifelong federated learning for machine recognition of speech
Bin HuUniversity of Illinois Urbana-ChampaignProvably robust adversarial reinforcement learning for sequential decision making in safety-critical environments
Lifu HuangVirginia TechEvent-centric temporal and causal knowledge acquisition and generalization for natural language understanding
Dinesh JayaramanUniversity of PennsylvaniaLearning modular dynamics models for plug-and-play visual control
Sven KoenigUniversity of Southern CaliforniaImproving planning and plan execution for warehouse automation
Laura KovacsTU WienFOREST: first-order reasoning for ensuring system security
Arun KumarUniversity of California, San DiegoImproving automated feature type inference for AutoML on tabular data
Himabindu LakkarajuHarvard UniversityTowards reliable and robust model explanations
Kevin Leyton-BrownUniversity of British ColumbiaAutomated machine learning for tabular datasets using hyperband embedded reinforcement learning
Bo LiUniversity of Illinois Urbana-ChampaignMachine learning evaluation as a service for robustness, fairness, and privacy utilities
Ke LiUniversity of ExeterMany hands make work light: multi-task deep semantic learning for testing web application firewalls
Zhiqiang LinOhio State UniversityType-aware recovery of symbol names in binary code: a machine learning based approach
Jeffrey LiuMassachusetts Institute of TechnologyIntegrating the low altitude disaster imagery (LADI) dataset into the MIT Beaver Works curriculum
Michael MahoneyUniversity of California, BerkeleySystematic methods for efficient inference and training of neural networks
Radu MarculescuUniversity of TexasNew directions for 3D object detection: distributed inference on edge devices using knowledge distillation
Ruben MartinsCarnegie Mellon UniversityImproving performance and trust of MaxSAT solvers
Jiri MatasCzech Technical University in PragueTraining neural networks on non-differentiable losses
Michael MilfordQueensland University of TechnologyComplementarity-aware multi-process fusion for long term localization
Heather MillerCarnegie Mellon UniversityDirected automated explicit-state model checking for distributed applications
Ndapa NakasholeUniversity of California, San DiegoLearning representations for voice-based conversational agents for older adults
Shrikanth NarayananUniversity of Southern CaliforniaToward inclusive human-AI conversational experiences for children
Lerrel PintoNew York UniversityLearning to manipulate deformable objects through robust simulations
Ravi RamamoorthiUniversity of California, San DiegoSparse multi-view object acquisition using learned volumetric representations
Philip ResnikUniversity of Maryland, College ParkAdvanced topic modeling to support the understanding of COVID-19 and its effects
Daniela RusMassachusetts Institute of TechnologyLearning to plan through imagined self-play for multi-agent system
Supreeth ShashikumarUniversity of California, San DiegoPrivacy preserving continual learning with applications to critical care
Robert ShepherdCornell UniversityEnduring and adaptive robots via electrochemical blood
Cong ShiUniversity of Michigan, Ann ArborMachine learning for personalized assortment optimization
Florian ShkurtiUniversity of TorontoGenerating physically realizable adversarial driving scenarios via differentiable physics and rendering simulators
Abhinav ShrivastavaUniversity of Maryland, College ParkThe pursuit of knowledge: discovering and localizing new concepts using dual memory
Roland SiegwartETH ZurichSafe self-calibration of hybrid aerial vehicles
Sameer SinghUniversity of California, IrvineDetecting and fixing vulnerabilities in NLP models via semantic perturbations and tracing data influence
Noah SmithUniversity of Washington - SeattleLanguage model customization
Mahdi SoltanolkotabiUniversity of Southern CaliforniaArtificial intelligence for fast and portable medical imaging (with limited training data)
Seung Woo SonUniversity of Massachusetts LowellReliable and accurate anomaly detection in edge nodes using sparsity profile
Dawn SongUniversity of California, BerkeleyKnowledge-enhanced cyber threat hunting
Dezhen SongTexas A&M University, College StationOptoacoustic material and structure pretouch sensing at robot fingertip
Shuran SongColumbia UniversityDexterity through diversity:learning a generalizable grasping policy for diverse end-effectors
Yizhou SunUniversity of California, Los AngelesAccelerating graph neural network training
Russ TedrakeMassachusetts Institute of TechnologyIntuitive physics for manipulation
James TompkinBrown UniversityReal-time multi-camera fusion for unoccluded VR robot teleoperation
Emina TorlakUniversity of Washington - SeattleAutomated verification of JIT compilers for BPF
Marynel VazquezYale UniversityEvaluating social robot navigation via online human-driven simulations
Nisheeth VishnoiYale UniversityFair and error-resilient algorithms for AI and ML
Gang WangUniversity of Illinois at Urbana–ChampaignCombating concept drift in security applications via proactive data synthesis
Hao WangRutgers University-New BrunswickStructured domain adaptation with applications to personalization and forecasting
James WangPennsylvania State UniversityAffective and social interaction between human and intelligent machine
Gloria WashingtonHoward UniversityTowards identification of uncomfortable speech in conversations
Chuan WuThe University of Hong KongCompilation optimization in distributed DNN training: joining OP and tensor fusion/partition
Eugene WuColumbia UniversityHuman-in-the-loop data debugging for ML-oriented analytics
Jiajun WuStanford UniversityImplicit dynamic scene representation learning for robotics
Ming-Ru WuDana-Farber Cancer InstituteFrom bench to clinic – machine-learning based cancer immunotherapy design
Diyi YangGeorgia Institute of TechnologyAbstractive conversation summarization at scale
Sixian YouMassachusetts Institute of TechnologyAI-driven label-free histology for cancer diagnosis
Jingjin YuRutgers University-New BrunswickPushing the limits of efficient and optimal multi-agent path finding through exploring space utilization optimization and adaptive planning horizon heuristics
Rui ZhangPennsylvania State UniversityBuilding robust conversational question answering systems over databases of tabular data
Yu ZhangUniversity of South FloridaDesign of an automated advanced air mobility flight planning system (AAFPS)
Yuke ZhuUniversity of Texas at AustinLearning implicit shape affordance for grasping and manipulation
Marinka ZitnikHarvard UniversityActionable graph learning for finding cures for emerging diseases
James ZouStanford UniversityHow to make AI forget you? Efficiently removing individuals’ data from machine learning models

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AWS Analytics is looking for a passionate, inventive Applied Scientist with a strong background in either machine learning, programming languages or databases to help create industry-leading analytics experiences powered by generative AI, machine learning, and program analysis. AWS provides a comprehensive set of analytics services for all data analytics needs and enables organizations of all sizes and industries to reinvent their business with data. From storage and management, data governance, actions, and experiences, AWS offers purpose-built services that provide the best price-performance, scalability, and lowest cost. We are a team dedicated to delivering transformative, science-driven analytics experiences for Amazon customers and having fun doing so. Our leadership team fosters an inclusive team culture and encourages work-life balance to bring out the best in each team member. Collaboration and mentorship are key tenets of our fabric. We are a growing team dedicated to supporting new members achieve their aspirations. Key job responsibilities As part of the AWS Analytics science team you will have the opportunity to apply your skills in machine learning, program analysis, and databases to impact some of the largest analytics services in the industry and their customers. You will innovate by designing and building agent-based solutions orchestrating foundation models, machine learning models, and program analyses to simplify AWS customers’ analytics journey and optimize their cost-performance profile. You will collaborate with a talented team of applied science peers to drive scientific impact and with engineering, product, and business leaders to launch your work in production at Amazon scale. A day in the life A mix of the following activities: talking to product leaders and customers to define science features; researching the state of the art and creating science plans to build them; building and rigorously benchmarking the science implementations of such features; partnering with engineering teams to onboard science work and launch it in production; preparing, publishing, and presenting scientific work at top-tier science venues and evangelizing it within the company; upgrading your science knowledge by participating in reading groups and science presentations by internal or external scientists; mentoring applied science interns and science peers in all of the above functions. About the team 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. 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. 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. 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.
US, WA, Seattle
Here at Amazon, we embrace our differences. We are committed to furthering our culture of diversity and inclusion of our teams within the organization. How do you get items to customers quickly, cost-effectively, and—most importantly—safely, in less than an hour? And how do you do it in a way that can scale? Our teams of hundreds of scientists, engineers, aerospace professionals, and futurists have been working hard to do just that! We are delivering to customers, and are excited for what’s to come. Check out more information about Prime Air on the About Amazon blog (https://www.aboutamazon.com/news/transportation/amazon-prime-air-delivery-drone-reveal-photos). If you are seeking an iterative environment where you can drive innovation, apply state-of-the-art technologies to solve real world delivery challenges, and provide benefits to customers, Prime Air is the place for you. Come work on the Amazon Prime Air Team! We are seeking a highly skilled Navigation Scientist to help develop advanced algorithms and software for our Prime Air delivery drone program. In this role, you will conduct comprehensive navigation analysis to support cross-functional decision-making, define system architecture and requirements, contribute to the development of flight algorithms, and actively identify innovative technological opportunities that will drive significant enhancements to meet our customers' evolving demands. Export Control License: This position may require a deemed export control license for compliance with applicable laws and regulations. Placement is contingent on Amazon’s ability to apply for and obtain an export control license on your behalf.
US, WA, Seattle
Do you want to create the greatest-possible worldwide impact in Robotics? Amazon has the world's most exciting treasure trove of robotics challenges. At Amazon Robotics we build high-performance, real-time robotic systems that can perceive, learn, and act intelligently alongside humans—at Amazon scale. Amazon Robotics invents and scales AI systems for robotics in fulfillment. Our mission is to enable robots to interact safely, efficiently, and fluently high density real-world fulfillment centers. Our AI solutions enable robots to learn from their own experiences, from each other, and from humans to build intelligence that feeds itself. We hire and develop collaborative subject matter experts in AI with a focus on computer vision, deep learning, semi-supervised and unsupervised learning. We target high-impact algorithmic unlocks in areas such as scene and activity understanding, large scale generative models, closed-loop control, robotic grasping and manipulation—all of which have high-value impact for our current and future fulfillment networks. We are seeking a hands-on, seasoned Applied Scientists who will be deep in code and algorithms; who are technically strong in building scalable vision systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation. As a Applied Scientist, you will contribute to the research and development of advanced robotic systems; your work along with other top-notch scientists and engineers will deliver the world's most scalable and robust robotic systems. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and active learning. As a Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on challenging customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a collaborative team environment. 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 Architect, design, and implement Machine Learning models for vision systems on robotic platforms Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. Work with stakeholders across , science, and operations teams to iterate on design and implementation. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment Work directly with customers and partners to test prototypes and incorporate feedback Mentor other engineer team members. A day in the life Amazon offers a full range of benefits for you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!