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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Are you interested in working with top talent in Optimization, Operations Research and Supply Chain to help Amazon to efficiently match our Devices with worldwide customers? We have challenging problems and need your innovative solutions to make tremendous financial impacts! The Amazon Devices Science team is looking for a Research Scientist with background in Operations Research, Optimization, Supply Chain and/or Simulation to support science efforts to integrate across inventory management functionalities. Our team is responsible for science models (both deterministic and stochastic) that power world-wide inventory allocation for Amazon Devices business that includes Echo, Kindle, Fire Tablets, Amazon TVs, Amazon Fire TV sticks, Ring, and other smart home devices. We formulate and solve challenging large-scale financially-based optimization problems which ingest demand forecasts and produce optimal procurement, production, distribution, and inventory management plans. In addition, we also work closely with demand forecasting, material procurement, production planning, finance, and logistics teams to co-optimize the inventory management and supply chain for Amazon Devices given operational constraints. Key job responsibilities The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail, and ability to work in a fast-paced and ever-changing environment and a desire to help shape the overall business. Job responsibilities include: - Design and develop advanced mathematical, simulation, and optimization models and apply them to define strategic and tactical needs and drive appropriate business and technical solutions in the areas of inventory management and distribution, network flow, supply chain optimization, and demand planning - Apply mathematical optimization techniques (linear, quadratic, SOCP, robust, stochastic, dynamic, mixed-integer programming, network flows, nonlinear, nonconvex programming) and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software - Research, prototype and experiment with these models by using modeling languages such as Python; participate in the production level deployment - Create, enhance, and maintain technical documentation, and present to other Scientists, Product, and Engineering teams - Support project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans - Influence organization's long-term roadmap and resourcing, and onboard new technologies onto the Science team's toolbox We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, MA, North Reading
Are you excited about developing generative AI and foundation models to revolutionize automation, robotics and computer vision? Are you looking for opportunities to build and deploy them on real problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience - at Amazon scale. We are looking for scientists, engineers and program managers for a variety of roles. The Research team at Amazon Robotics is seeking a passionate, hands-on Sr. Applied Scientist to help create the world’s first foundation model for a many-robot system. The focus of this position is how to predict the future state of our warehouses that feature a thousand or more mobile robots in constant motion making deliveries around the building. It includes designing, training, and deploying large-scale models using data from hundreds of warehouses under different operating conditions. This work spans from research such as alternative state representations of the many-robot system for training, to experimenting using simulation tools, to running large-scale A/B tests on robots in our facilities. Key job responsibilities * Research vision - Where should we be focusing our efforts * Research delivery - Proving/dis-proving strategies in offline data or in simulation * Production studies - Insights from production data or ad-hoc experimentation * Production implementation - Building key parts of deployed algorithms or models About the team You would join our multi-disciplinary science team that includes scientists with backgrounds in planning and scheduling, grasping and manipulation, machine learning, and operations research. We develop novel planning algorithms and machine learning methods and apply them to real-word robotic warehouses, including: - Planning and coordinating the paths of thousands of robots - Dynamic allocation and scheduling of tasks to thousands of robots - Learning how to adapt system behavior to varying operating conditions - Co-design of robotic logistics processes and the algorithms to optimize them Our team also serves as a hub to foster innovation and support scientists across Amazon Robotics. We also coordinate research engagements with academia, such as the Robotics section of the Amazon Research Awards. We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA | Westborough, MA, USA
US, WA, Bellevue
Inventory Planning and Control (IPC) is seeking an experienced senior data scientist to join its central science team. Our team owns the core decision models in the space of Buying, Placement, and Capacity Control. Our models decide when, where, and how much we should buy, flow, and hold inventories in our global fulfillment network to meet Amazon’s business goals and to make our customers happy. We do this for hundreds of millions of items and hundreds of product lines worth billions of dollars of world-wide for both our Retail and third-party seller business. Our systems are built entirely in-house, for which we constantly develop new technologies in automated inventory planning, prediction, optimization and simulation. Our systems operate at various scales, from real-time decision system that completes thousands of transactions per seconds, to large scale distributed system that optimizes the inventory decisions over millions of products simultaneously. IPC is also unique in that we are simultaneously developing the science and software of inventory optimization and solving some of the toughest computational/operational challenges in production. Our team members have an opportunity to be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, scientists, and software engineers in the industry. Key job responsibilities Candidates will be responsible for developing causal, machine learning and data driven models to enhance the various inventory optimization engines that the team owns. The successful candidate should have solid hands-on experience in applying machine learning or causal inference models. They will also be responsible for conducting data driven analysis to facilitate strategic decisions. They require superior logical thinkers who are able to quickly approach large ambiguous problems and develop a practical plan to tackle. Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving. They are able to measure and estimate risks, and constructively critique peer research. As a senior scientist, you will also help coach/mentor junior scientists in the team. A day in the life The IPC science team contains a large group of scientists with different technical expertise, who will help and collaborate with you on your projects. In this role, you will also work with our internal customers from the Retail, third-party seller and operations departments worldwide. You will understand their challenges and pain points, and help develop data driven solutions that improve how Amazon manages inventory in our global supply chain. You will work closely with the product managers, engineers and other scientists to turn science proposals into production implementation. About the team We are a team of scientists, product managers and engineers focusing on innovation. We promote experimentation and learn by building. We often tackle the hardest problem in the organization and work cross-functionally. We are at the center of developing inventory solutions to support the rapid growth of Amazon's store business. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA