98 Amazon Research Awards recipients announced

Awardees, who represent 51 universities in 15 countries, have access to Amazon public datasets, along with AWS AI/ML services and tools.

Amazon Research Awards (ARA) provides unrestricted funds and AWS Promotional Credits to academic researchers investigating various research topics in multiple disciplines. This cycle, ARA received many excellent research proposals from across the world and today is publicly announcing 98 award recipients who represent 51 universities in 15 countries.

This announcement includes awards funded under six call for proposals during the fall 2023 cycle: AI for Information Security, Automated Reasoning, AWS AI, AWS Cryptography and Privacy, AWS Database Services, and Sustainability. Proposals were reviewed for the quality of their scientific content and their potential to impact both the research community and society.

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.

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.

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“We received a fantastic response to the cryptography and privacy engineering’s call for proposals. This was the first time we offered ARAs for cryptography and privacy, and the response far exceeded our expectations, in terms of both the number and quality of the proposals,” said Rod Chapman, senior principal applied scientist with AWS Cryptography. “Advanced cryptography plays a crucial role in building trust with our customers and regulators, especially in emerging domains such as cryptographic computing, generative AI, and privacy-preserving applications. We look forward to working with the new principal investigators to bring ever more impactful cryptographic technologies to fruition.”

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“Given that data is central to Amazon’s core businesses, I am excited by this opportunity to collaborate with universities on cutting-edge technologies for modern database systems,” said Doug Terry, vice president and distinguished scientist in AWS Database and AI Leadership. “These Amazon Research Awards allow us to support projects that have the potential for substantial advancement in important areas from correctness testing of SQL queries to new data models for generative AI applications.”

ARA funds proposals throughout the 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 tables below list, in alphabetical order by last name, fall 2023 cycle call-for-proposal recipients, sorted by research area.

AI for Information Security

Photo grid shows the recipients of the 2023 fall AI for information security Amazon Research Awards

RecipientUniversityResearch title
Murat KocaogluPurdue UniversityCausal Anomaly Detection from Non-stationary Time-series in the Cloud
Hui LiuMichigan State UniversityHarnessing the Power of Weakly-Supervised Graph Representation Learning for Cybersecurity
Xiaorui LiuNorth Carolina State UniversityHarnessing the Power of Weakly-Supervised Graph Representation Learning for Cybersecurity
Thomas PasquierUniversity of British ColumbiaBuilding Robust Provenance-based Intrusion Detection
Michalis PolychronakisStony Brook UniversitySafeTrans: AI-assisted Transcompilation to Memory-safe Languages

Automated Reasoning

Photo grid shows the recipients of the 2023 fall automated reasoning Amazon Research Awards

RecipientUniversityResearch title
Victor BrabermanUniversidad de Buenos AiresAbstractions for Validating Distributed Protocol Reference Implementations
Varun ChandrasekaranUniversity of Illinois Urbana-ChampaignAutomating Privacy Compliance
Maria ChristakisTU WienTesting Dafny for Unsoundness and Brittleness Bugs
Werner DietlUniversity of WaterlooOptional Type Systems for Model-Implementation Consistency
Alastair DonaldsonImperial College LondonValidating Compilers for the Dafny Verified Programming Language
Azadeh FarzanUniversity of TorontoBetter Predictability in Dynamic Data Race Detection
Sicun GaoUniversity Of California, San DiegoProof Optimization and Generalization in dReal
Tobias GrosserUniversity Of CambridgeCorrect and High-Performance Domain-Specific Compilation with Lean and MLIR
Andrew HeadUniversity Of PennsylvaniaTYCHE: An IDE for Property-Based Testing
Kihong HeoKorea Advanced Institute Of Science and Technology - KAISTGenerative Translation Validation for JIT Compiler in the V8 JavaScript Engine
Frans KaashoekMassachusetts Institute of TechnologyFlotilla: Compositional Formal Verification of Liveness of Distributed Systems Implementations
Baris KasikciUniversity of Washington - SeattlePrivacy-Conscious Failure Reproduction for Root Cause Diagnosis in Large-Scale Distributed Systems
Laura KovacsTU WienQuAT: Quantifiers with Arithmetic Theories are Friends with Benefits
Shriram KrishnamurthiBrown UniversityParalegal: Scalable Tooling to Find Privacy Bugs in Application Code
Corina PasareanuCarnegie Mellon UniversityProving the Absence of Timing Side Channels in Cryptographic Applications
Jean Pichon-PharabodAarhus UniversityValidating Isolation of Virtual Machines in the Cloud
Benjamin PierceUniversity Of PennsylvaniaTYCHE: An IDE for Property-Based Testing
Ruzica PiskacYale UniversityDemocratizing the Law - Using LLMs and Automated Reasoning for Legal Reasoning
Malte SchwarzkopfBrown UniversityParalegal: Scalable Tooling to Find Privacy Bugs in Application Code
Peter SewellUniversity Of CambridgeThe Foundations of Cloud Virtual-machine Isolation
Scott ShapiroYale UniversityDemocratizing the Law - Using LLMs and Automated Reasoning for Legal Reasoning
Geoffrey SutcliffeUniversity Of MiamiAutomated Theorem Proving Community Infrastructure in the AWS Cloud
Joseph TassarottiNew York UniversityAsynchronous Couplings for Probabilistic Relational Reasoning in Dafny
Sebastian UchitelUniversidad de Buenos AiresAbstractions for Validating Distributed Protocol Reference Implementations
Josef UrbanCzech Technical UniversityLearning Based Synthesis Meets Learning Guided Reasoning
Thomas WiesNew York UniversityAutomating Privacy Compliance
Nickolai ZeldovichMassachusetts Institute of TechnologyFlotilla: Compositional Formal Verification of Liveness of Distributed Systems Implementations

AWS AI

Photo grid shows the recipients of the 2023 fall AWS AI Amazon Research Awards

RecipientUniversityResearch title
Pulkit AgrawalMassachusetts Institute Of TechnologyAdapting Foundation Models without Finetuning
Niranjan BalasubramanianStony Brook UniversityAn API Sandbox for Complex Tasks on Common Applications
Osbert BastaniUniversity Of PennsylvaniaUncertainty Quantification for Trustworthy Language Generation
Matei CiocarlieColumbia UniversityDo You Speak EMG? Generative Pre-training on Electromyographic Signals for Controlling a Rehabilitation Robot after Stroke
Caiwen DingUniversity of ConnecticutGraph of Thought: Boosting Logical Reasoning in Large Language Models
Yufei DingUniversity Of California, San DiegoA Hollistic Compiler and Runtime System for Efficient and Scalable LLM Serving
Xinya DuUniversity Of Texas At DallasProcess-guided Fine-tuning for Answering Complex Questions
Luciana FerrerUniversity of Buenos Aires - CONICETEfficient Adaptation of Generative Language Models through Unsupervised Calibration
Jakob FoersterUniversity Of OxfordCompute-only Scaling of Large Language Models
Nikhil GargCornell UniversityRecommendation systems in high-stakes settings
Georgia GkioxariCalifornia Institute Of TechnologyTowards a 3D Foundation Model: Recognize and Reconstruct Anything
Tom GoldsteinUniversity of MarylandBuilding Safer Diffusion Models
Albert GuCarnegie Mellon UniversityScaling the Next Generation of Foundation Model Architectures
Mahdi S. HosseiniConcordia UniversityToward Auto-Populating Synoptic Reports in Diagnostic Pathology
Maliheh IzadiDelft University Of TechnologyUnderstanding and Regulating Memorization in Large Language Models for Code
Vijay Janapa ReddiHarvard UniversityBenchmarking the Safety of Generative AI Models with Data-centric AI Challenges
Adel JavanmardUniversity of Southern CaliforniaReliable AI for Generation of Medical Reports from MRI Scans
Jianbo JiaoUniversity Of BirminghamPCo3D: Physically Plausible Controllable 3D Generative Models
Subbarao KambhampatiArizona State UniversityUnderstanding and Leveraging Planning, Reasoning & Self-Critiquing Capabilities of Large Language Models
Kangwook LeeUniversity Of Wisconsin–MadisonInformation and Coding Theory-Based Framework for Prompt Engineering
Ales LeonardisUniversity Of BirminghamPCo3D: Physically Plausible Controllable 3D Generative Models
Anqi LiuJohns Hopkins University(Multi-)Calibrated Active Learning under Subpopulation Shift
Lydia LiuPrinceton UniversityFrom Predictions to Positive Impact: Foundations of Responsible AI in Social Systems
Pablo PiantanidaNational Centre for Scientific Research (CNRS)Efficient Adaptation of Generative Language Models through Unsupervised Calibration
Chara PodimataMassachusetts Institute Of TechnologyResponsible AI through User Incentive-Awareness
Bhiksha RajCarnegie Mellon UniversityText and Speech Large Language Models
Christian RupprechtUniversity Of OxfordViewset Diffusion for Probabilistic 3D Reconstruction
Olga RussakovskyPrinceton UniversityDiffusion models: Generative models beyond data generation
Vatsal SharanUniversity Of Southern CaliforniaDebiasing ML-based Decision Making using Multicalibration
Abhinav ShrivastavaUniversity Of MarylandAudio-conditioned Diffusion Models for Generating Lip-synchronized Videos
Rachee SinghCornell UniversityAccelerating collective communication for distributed ML
Vincent SitzmannMassachusetts Institute Of Technology2D and 3D Animation via Image-Conditional Generative Flow Models
Justin SolomonMassachusetts Institute Of TechnologyLightweight Algorithms for Generative AI
Mahdi SoltanolkotabiUniversity of Southern CaliforniaReliable AI for Generation of Medical Reports from MRI Scans
Qian TaoDelft University of TechnologyΦ-Generative Medical Imaging by Physics and AI (PhAI)
Yapeng TianUniversity Of Texas At DallasIntegrating Visual Alignment and Text Interaction for Multi-modal Audio Content Generation
Sherry Tongshuang WuCarnegie Mellon UniversityGenerating Deployable Models from Natural Language Instructions through Adaptive Data Curation
Florian TramerEth ZurichCan Technology Protect us from Generative AI?
Arie van DeursenDelft University Of TechnologyUnderstanding and Regulating Memorization in Large Language Models for Code
Andrea VedaldiUniversity Of OxfordViewset Diffusion for Probabilistic 3D Reconstruction
Carl VondrickColumbia UniversityViper: Visual Inference via Python Execution for Reasoning
Xiaolong WangUniversity of California, San DiegoGenerating Compositional 3D Scenes and Embodied Tasks with Large Language Models
Eric WongUniversity Of PennsylvaniaAdversarial Manipulation of Prompting Interfaces
Saining XieNew York UniversityImage Sculpting: Precise Image Generation and Editing with Interactive Geometry Control
Minlan YuHarvard UniversityTroubleshooting Distributed Training Systems
Zhiru ZhangCornell UniversityA Unified Approach to Tensor Graph Optimization

AWS Cryptography and Privacy

Photo grid shows the recipients of the 2023 fall AWS Cryptography and Privacy Amazon Research Awards

RecipientUniversityResearch title
Christopher BrzuskaAalto UniversitySecure Messaging: Updates Efficiency & Verification
Tevfik BultanUniversity of California, Santa BarbaraDetecting and Quantifying Information Leakages in Crypto Libraries
Muhammed EsginMonash UniversityPractical Post-Quantum Oblivious Pseudorandom Functions Supporting Verifiability
Nadia HeningerUniversity of California, San DiegoBringing Modern Security Guarantees to End-to-End Encrypted Cloud Storage
Tal MalkinColumbia UniversityCryptographic Techniques for Machine Learning
Peihan MiaoBrown UniversityAdvancing Private Set Intersection for Wider Industrial Adoption
Virginia SmithCarnegie Mellon UniversityRethinking Watermark Embedding and Detection for LLMs
Ron SteinfeldMonash UniversityPractical Post-Quantum Oblivious Pseudorandom Functions Supporting Verifiability

AWS Database Services

Photo grid shows the recipients of the fall 2023 AWS Database Services Amazon Research Awards

RecipientUniversityResearch title
Lei CaoUniversity Of ArizonaSEED: Simple, Efficient, and Effective Data Management via Large Language Models
Samuel MaddenMassachusetts Institute Of TechnologySEED: Simple, Efficient, and Effective Data Management via Large Language Models
Manuel RiggerNational University Of SingaporeDemocratizing Database Fuzzing

Sustainability

Photo grid shows the recipients of the fall 2023 sustainability Amazon Research Awards

RecipientUniversityResearch title
Kate ArmstrongNew York Botanical GardenVERDEX: remote sensing of plant biodiversity
Praveen BolliniUniversity Of HoustonData-driven design and optimization of selective nanoporous catalysts for biofuel conversion
Brandon BukowskiJohns Hopkins UniversityData-driven design and optimization of selective nanoporous catalysts for biofuel conversion
Alan EdelmanMassachusetts Institute of TechnologyScientific Machine Learning with Application to Probabilistic Climate Forecasting and Sustainability
Vikram IyerUniversity of Washington - SeattleData-Driven Sustainable Polymer Design for Circuits, Packaging, and Actuators
Can LiPurdue UniversityDesign and Analysis of Sustainable Supply Chains Using Optimization and Large Language Models
Damon LittleNew York Botanical GardenVERDEX: remote sensing of plant biodiversity
Aniruddh VashisthUniversity of Washington - SeattleData-Driven Sustainable Polymer Design for Circuits, Packaging, and Actuators
Ming XuTsinghua UniversityAdvancing Sustainable Practices in the AI Era: Integrating Large Language Models for Automated Life Cycle Assessment Modeling

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Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center at AWS is a new strategic team that helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, data scientists, engineers, and solution architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Data Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. A key focus of this role is GenAI model customization using techniques such as fine-tuning and continued pre-training to help customers build differentiating solutions with their unique data. Key job responsibilities As a Data Scientist, you will: Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder Provide customer and market feedback to Product and Engineering teams to help define product direction About the team Sales, Marketing and Global Services (SMGS) AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest-growing small- and mid-market accounts to enterprise-level customers, including the public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. The Professional Services team is part of Global Services. About AWS 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. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Denver, CO, USA | Herndon, VA, USA | New York, NY, USA | Santa Clara, CA, USA | Seattle, WA, USA | Washington Dc, DC, USA
GB, London
Re-imagining the realms of what’s possible in advertising. Amazon is re-imagining advertising. Amazon Ads operates at the intersection of eCommerce and advertising and offering a rich array of advertising solutions and audience insights so businesses and brands can create relevant campaigns that produce measurable results. At Amazon Ads, you can build models that impact millions every day. And we’re passionate about solving real-world problems while using cutting-edge machine learning and artificial intelligence to do this. For example, our applied science teams leverage a variety of advanced machine learning and cloud computing techniques to power Amazon's advertising offerings. This includes building algorithms and cloud services using clustering, deep neural networks, and other ML approaches to make ads more relevant while respecting privacy. They develop machine learning models to predict ad outcomes and select the optimal ad for each shopper, context, and advertiser objective, leveraging techniques like multi-task learning, bandit/reinforcement learning, counterfactual estimation, and low-latency extreme ML. The teams also utilize Spark, EMR, and Elasticsearch to extract insights from big data and deliver recommendations to advertisers at scale, continuously improving through offline analysis and impact evaluation. Additionally, they apply generative AI models for dynamic creative optimization and video experimentation and automation. Underpinning these efforts are unique technical challenges, such as operating at unprecedented scale (hundreds of thousands of requests per second with 40ms latency) while respecting privacy and customer trust guarantees, and solving a wide variety of complex computational advertising problems related to traffic quality, viewability, brand safety, and more. Help us take innovation in advertising to the next level. Our teams are based in our fast-growing tech hubs in London and Edinburgh. Learn more about Amazon Ads, employee stories and available opportunities here: https://www.amazon.jobs/content/en/teams/advertising/applied-science-machine-learning-research?ref_=a20m_us_car_lp_asml Key job responsibilities * Design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both analysis and business judgment. * Collaborate with software engineering teams to integrate successful experiments into large-scale, highly complex Amazon production systems. * Promote the culture of experimentation and applied science at Amazon. * Demonstrate ability to meet deadlines while managing multiple projects. * Excel communication and presentation skills working with multiple peer groups and different levels of management * Influence and continuously improve a sustainable team culture that exemplifies Amazon’s leadership principles * Develop a deep and wide understanding of large ad tech solutions to which you will contribute, and how they interact with components owned by other teams. * Anticipate obstacles and look around corners, effectively prioritising work, solving trade-offs and influencing the development of advertising products beyond the scope of your immediate team. We are open to hiring candidates to work out of one of the following locations: Edinburgh, MLN, GBR | London, GBR
CN, 11, Beijing
Amazon Search JP builds features powering product search on the Amazon JP shopping site and expands the innovations to world wide. As an Applied Scientist on this growing team, you will take on a key role in improving the NLP and ranking capabilities of the Amazon product search service. Our ultimate goal is to help customers find the products they are searching for, and discover new products they would be interested in. We do so by developing NLP components that cover a wide range of languages and systems. As an Applied Scientist for Search JP, you will design, implement and deliver search features on Amazon site, helping millions of customers every day to find quickly what they are looking for. You will propose innovation in NLP and IR to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. You will then integrate these models into the production search engine that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times. Key job responsibilities - Designing and implementing new features and machine learned models, including the application of state-of-art deep learning to solve search matching, ranking and Search suggestion problems. - Analyzing data and metrics relevant to the search experiences. - Working with teams worldwide on global projects. Your benefits include: - Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers - The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems with tangible customer impact - Being part of a growing team where you can influence the team's mission, direction, and how we achieve our goals We are open to hiring candidates to work out of one of the following locations: Beijing, 11, CHN | Shanghai, 31, CHN