{"slug": "amazon-research-awards-recipients-announced", "title": "Amazon Research Awards recipients announced", "summary": "Amazon announced 70 recipients of its Amazon Research Awards from 49 universities across 11 countries, funding research in AI for information security, agentic AI, automated reasoning, cryptography, cybersecurity, and sustainability. The awards provide unrestricted funds, AWS Promotional Credits, and access to Amazon's public datasets and AI services to support academic research with potential societal impact. The program aims to advance fraud prevention, cybersecurity, and other critical fields by fostering collaboration between industry and academia.", "body_md": "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 70 award recipients who represent 49 universities in 11 countries.\n\nThis announcement includes awards funded under 6 calls for proposals during the fall 2025 cycle: [AI for Information Security](https://www.amazon.science/research-awards/call-for-proposals/ai-for-information-security-call-for-proposals-fall-2025), [Agentic AI](https://www.amazon.science/research-awards/call-for-proposals/aws-agentic-ai-call-for-proposals-fall-2025) , [Automated Reasoning](https://www.amazon.science/research-awards/call-for-proposals/automated-reasoning-call-for-proposals-fall-2025), [AWS Cryptography](https://www.amazon.science/research-awards/call-for-proposals/aws-cryptography-call-for-proposals-fall-2025), [Cybersecurity and Anti-Abuse Technologies](https://www.amazon.science/research-awards/call-for-proposals/cybersecurity-research-and-anti-abuse-technologies-call-for-proposals-fall-2025), and [Sustainability](https://www.amazon.science/research-awards/call-for-proposals/sustainability-call-for-proposals-fall-2025) 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.\n\nRecipients have access to more than 700 [Amazon public datasets](https://aws.amazon.com/opendata/?wwps-cards.sort-by=item.additionalFields.sortDate&wwps-cards.sort-order=desc) 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.\n\n\"Fraud and abuse evolve at the speed of the technologies that bad actors exploit. Since we can only defend against what we can measure, the science of studying those technologies has to keep pace,\" said [Dhruv Kuchhal](https://www.amazon.science/author/dhruv-kuchhal), Applied Scientist, Special Projects & Invest-Fixed. \"Through ARA, we bring together experts across industry and academia to tackle these problems upstream and publish defenses that systematically raise bad actors' operating costs and erode their ROI as they spread across the ecosystem. This not only strengthens Amazon, but the broader Web, including online shopping customers, sellers and brands who build businesses online, and the platforms and payment rails that tie them together. We were impressed by the quality and volume of proposals we received — a strong signal that the field is raising the bar for Web users everywhere — and we look forward to working with the new recipients to turn this research into lasting, ecosystem-wide improvements in fraud and abuse prevention.\"\n\n“AI is reshaping cybersecurity faster than ever in advancing how we detect threats and defend systems, ”said Wei Ding, Applied Science Manager, GuardDuty, AWS. “At the same time, agentic AI requires stronger guarantees of safety, robustness, and trust worthiness. Since 2020, our team has funded security research that solves some of the biggest challenges for the industry. We’re pleased to continue our tradition of fostering innovation through these latest research projects addressing agentic AI security, AI-powered incident response, and threat detection in agentic AI systems and cloud environments, among other exciting areas.”\n\nARA funds proposals throughout the year in a variety of research areas. Applicants are encouraged to visit our [call for proposals page](https://www.amazon.science/research-awards/call-for-proposals) for more information or [send an email](mailto:research-awards@amazon.com) to be notified of future open calls.\n\nThe tables below list, in alphabetical order by last name, fall 2025 cycle call-for-proposal recipients, sorted by research\n\n**AI for Information Security**\n\n|\n|\n|\nVirginia Polytechnic Institute and State University |\nCortexCTI: A Unified Threat Intelligence Engine for Knowledge-Driven Cloud Threat Detection and Response |\n|\nTexas A&M University |\nNew Benchmark and Defense on Prompt Injection in Agentic AI Systems |\n|\nArizona State University |\nSecuring Agentic AI: From Local Detection to Global Assurance |\n|\nThe University of Edinburgh |\nExploit-driven AI Agents for vulnerability detection verification |\n|\nUniversity of Southern California |\nSecuring Agentic AI: From Local Detection to Global Assurance |\n\n**Automated Reasoning**\n\n|\n|\n|\nCarnegie Mellon University |\nA Visual Debugger for Program Verification |\n|\nImperial College London |\nSOLAR: Symbolic Learning for Automated Requirements Consistency |\n|\nUniversity of Verona |\nNew Data Structure Theories and Quantifiers in CDSAT |\n|\nUniversity of California Los Angeles |\nBreaking the Parallelism Limit with SAT-solving Accelerators |\n|\nThe University of Manchester |\nCombining Formal Methods with Large Language Models in ESBMC: Enabling Automated Program Verification through AI/ML |\n|\nUniversity of Waterloo |\nStrata-Sphere: Expressive Type Systems and Language Formalizations |\n|\nTU Wien |\nPASSAT: Improved Passing of Assertion Stacks to SAT in Incremental SMT Solvers |\n|\nUniversity of California San Diego |\nEvaluating and Improving Quantitative Reasoning in LLM Agents Using Sandbox Coding Tasks and Formal Tools |\n|\nThe University of Texas at Austin |\nDocumenting and Recommending Tactics in HOL Light |\n|\nColumbia University in the City of New York |\nScaling Formal Verification of Security Properties for Unmodified System Software |\n|\nUniversity of Maryland Baltimore County |\nAutoformalization for Scientific Computing in Lean |\n|\nThe University of Texas at Austin |\nDocumenting and Recommending Tactics in HOL Light |\n|\nNorth Carolina State University |\nNeurosymbolic LLM Reasoning with Symbolical Soundness and Logical Consistency |\n|\nImperial College London |\nSoteria in Lean: Mechanising the Next Generation of Symbolic Execution Tools |\n|\nKarlsruhe Institute of Technology |\nResource-Efficient Flexible SAT Solving in HPC and Cloud Environments |\n|\nNational University of Singapore |\nLinear Types for a Foundational Multi-Modal Program Verifier |\n|\nUniversity of Cambridge |\nGradual Lightweight Methods for High-Assurance Cloud Infrastructure |\n|\nSyracuse University |\nNon-Markovian Agentic Meta-Reasoning |\n|\nMassachusetts Institute of Technology |\nSynthesizing Library Models for Static Analysis via LLMs and Conformance Testing |\n|\nHarvard University |\nTranslating Formal Proofs of Differential Privacy via LLMs |\n|\nMassachusetts Institute of Technology |\nVerifying Rust distributed system implementations using monotonic ownership state machines in Verus |\n|\nBoston University |\nAuto-Formalization and Informalization through Two-Stage Reinforcement Learning |\n|\nPurdue University |\nScaling Interprocedural Data-Flow Analysis with LLMs |\n\n**AWS Agentic AI**\n\n|\n|\n|\nJohns Hopkins University |\nMulti-Party Differential Privacy: Unlocking Enterprise Agentic AI |\n|\nWorcester Polytechnic Institute |\nAutonomous Catalyst Design with Agentic AI for Hydrogen Production |\n|\nUniversity of California Davis |\nFlowGuard: Evolutionary Red-Teaming for Safe Multimodal Web Agents |\n|\nUniversity of California Santa Cruz |\nCAMEO: Confidential Agentic Multi-component Enclave Orchestration |\n|\nUniversity of Minnesota Twin Cities |\nEnd-to-End Agentic AI for Scalable Chiplet Design with Extreme Parallelism and Heterogeneity |\n|\nBen-Gurion University of the Negev |\nMulti-Agent Pathfinding with Unassigned Agents |\n|\nThe University of Texas at Austin |\nFrom Observation to Intervention: Counterfactual Multi-Agent World Models for Autonomous Driving |\n|\nThe University of Tennessee-Knoxville |\nBeyond Walls of Text: Building UI-Native LLM Agents as the Next Gateway to the Internet |\n|\nUniversity of Illinois at Urbana-Champaign |\nReinforcing Coordination: Streaming, Exploration, and Distillation for Long-Horizon Agent Learning |\n|\nJohns Hopkins University |\nA Protocol Stack for Resource-Bound Multi-Agent AI |\n|\nUniversity of Michigan |\nAutomating Large Scale Deployment of Infrastructure-based Safety Critical Event Detection with Agentic AI |\n|\nNational University of Singapore |\nSelf-Configurable Agentic Learning via Co-optimization |\n|\nJohns Hopkins University |\nMarkov Near-Potential Function Based MARL Training for Mixed Cooperative–Competitive Agentic AI |\n|\nTexas A&M University |\nA Retrieval-Augmented Dual-Attention Vision Framework for Standards-Aligned Infrastructure Inspection |\n|\nNew York University Abu Dhabi |\nAVAAS – Automated Vulnerability Analysis Through Advanced Agentic Systems |\n|\nBen-Gurion University of the Negev |\nMulti-Agent Pathfinding with Unassigned Agents |\n|\nTexas A&M University |\nFlowGuard: Evolutionary Red-Teaming for Safe Multimodal Web Agents |\n|\nUniversity of Michigan |\nBenchmarking and Monitoring Multi-Agent Scheming |\n|\nRice University |\nEmpowering Multimodal AI Agents with Continuous Learning |\n|\nUniversity of Massachusetts Amherst |\nA Framework for Proactive and Collaborative AI Agents |\n|\nUniversity of Minnesota Twin Cities |\nEnd-to-End Agentic AI for Scalable Chiplet Design with Extreme Parallelism and Heterogeneity |\n|\nUniversity of Waterloo |\nKNOWLEDGESTORE: A Dynamic Hierarchical Memory for Scalable, Enterprise-Ready AI Agents on AWS |\n\n**AWS Cryptography**\n\n|\n|\n|\nThe University of Sydney |\nEfficient Robust Post-Quantum Distributed Key Generation and Threshold Signatures |\n|\nUniversity of Illinois at Chicago |\nFormally verified symmetric cryptography |\n|\nPurdue University |\nStronger Memory Hard Functions to Protect Passwords against Brute Force Attacks |\n|\nParis Cité University |\nPseudorandom Correlations for Threshold Cryptography |\n|\nNew York University |\nMachine Unlearning and Computational Assumptions for AI |\n|\nNortheastern University - United States of America |\nPractical Watermarking for LLMs via Pseduorandom Codes |\n|\nMassachusetts Institute of Technology |\nEnhancing AI Safety Using Cryptography |\n|\nBoston University |\nPushing secure MPC beyond niche applications |\n|\nUniversity of California Los Angeles |\nTowards Low-Latency Maliciously Secure MPC for LLMs |\n|\nRoyal Holloway - University of London |\nNew Approaches for the Linear Transform in BFV/BGV |\n|\nCarnegie Mellon University |\nPractical Secure Computation At Scale |\n|\nUniversity of Toronto |\nSimultaneous-Message and Succinct Secure Computation |\n|\nUniversity of Waterloo |\nFantASM: Fast, Auditable, and Neat Assembly |\n|\nArizona State University |\nFuzzy Secure Computation for Real-World Noisy Data |\n|\nNorthwestern University - United States of America |\nFrom Signing to Garbling: Exploring the Spectrum of Post-Quantum Primitives |\n|\nStanford University |\nAlgorithms for Post-Quantum Cryptography |\n|\nNational University of Singapore |\nPractical Watermarking for LLMs via Pseduorandom Codes |\n|\nGeorgia Institute of Technology |\nFuzzy Secure Computation for Real-World Noisy Data |\n\n**Cybersecurity and Anti-Abuse Technologies**\n\n|\n|\n|\nUniversity of California San Diego |\nDetecting Anti-detect Browsers at Scale |\n\n**Devices Sustainability**\n\n|\n|\n|\nCornell University |\nAgent-Driven Life Cycle Carbon Optimization for Sustainable Edge Devices |\n|\nBrown University |\nIntegrating Sustainability Reasoning into Early-Stage Electronics Design |", "url": "https://wpnews.pro/news/amazon-research-awards-recipients-announced", "canonical_source": "https://www.amazon.science/research-awards/latest-news/fall-2025-amazon-research-awards-recipients-announced", "published_at": "2026-05-27 17:21:51+00:00", "updated_at": "2026-06-11 20:11:46.494367+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-research", "ai-ethics", "ai-safety", "ai-agents"], "entities": ["Amazon Research Awards", "Amazon", "AWS"], "alternates": {"html": "https://wpnews.pro/news/amazon-research-awards-recipients-announced", "markdown": "https://wpnews.pro/news/amazon-research-awards-recipients-announced.md", "text": "https://wpnews.pro/news/amazon-research-awards-recipients-announced.txt", "jsonld": "https://wpnews.pro/news/amazon-research-awards-recipients-announced.jsonld"}}