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63 Amazon Research Award recipients announced

Awardees, who represent 41 universities in 8 countries, have access to Amazon public datasets, along with AWS AI/ML services and tools.

By Amazon Research Awards team
November 25, 2025
8 min read
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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 63 award recipients who represent 41 universities in 8 countries.

This announcement includes awards funded under five call for proposals during the spring 2025 cycle: AI for Information Security, Amazon Ads, AWS AI: Agentic AI, Build on Trainium and Think Big. 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 700 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.

Rule generation subgraph
How Amazon uses AI agents to anticipate and counter cyber threats
Amazon's competitive-agent architecture creates a continuous improvement cycle that develops security protections at machine speed, reducing what typically takes weeks down to hours.

"Amazon Research Awards are enabling incredibly impactful work to improve human health—from revolutionizing and democratizing structural biology tools, which can accelerate discovery of candidate molecules for new drugs to help patients, to predicting the etiology of a stroke in order to start the appropriate therapies, or interpreting digital phenotyping data to help with mental health services," said Christine Silvers, AWS Principal Healthcare Advisor. "These are just three examples of projects that recipients have received Amazon Research Awards for. The potential for improving healthcare amongst all of the spring 2025 plus past and future awardees is staggering and inspiring.“

"Academic AI researchers face a fundamental challenge: advancing machine learning research and educating the next generation requires access to cutting-edge infrastructure that's both powerful and affordable," said Yida Wang, AWS AI Principal Applied Scientist. "The Build on Trainium program directly addresses this barrier. We are working with leading AI research universities such as, UC Berkeley, Stanford, CMU, MIT, UIUC, UCLA, and many others.  At CMU, researchers achieved significant improvements over state-of-the-art FlashAttention in just one week. At MIT, researchers trained 3D medical imaging models with 50% higher throughput and lower cost, reducing training time from months to weeks. Build on Trainium represents AWS's commitment to democratizing AI research through collaborative partnership with academia—fostering an environment where researchers experiment freely, students learn on production-scale infrastructure, and academic innovations shape the future of machine learning for everyone."

The tables below list, in alphabetical order by last name, the spring 2025 cycle call-for-proposal recipients, sorted by research area.

AI for Information Security

ARA-AIInfoSecurity-1200x750-02.png

Recipient

University

Research title

Christopher Fletcher

University Of California, Berkeley

Design and Verification of High-Assurance Key Management Services for Stateful Confidential Computing

Zhou Li

University Of California, Irvine

Precise and Analyst-friendly Attack Provenance on Audit Logs with LLM

Yu Meng

University of Virginia

Weakly-Supervised RLHF: Modeling Ambiguity and Uncertainty in Human Preferences

Jelena Mirkovic

University of Southern California

Safe and Secure API Discovery for Agentic AI

Aanjhan Ranganathan

Northeastern University

Understanding How LLMs Hack: Interpretable Vulnerability Detection and Remediation

Sanjit Seshia

University Of California, Berkeley

Design and Verification of High-Assurance Key Management Services for Stateful Confidential Computing

Alexey Tregubov

University of Southern California

Safe and Secure API Discovery for Agentic AI

Ziming Zhao

Northeastern University

Understanding How LLMs Hack: Interpretable Vulnerability Detection and Remediation

Amazon Ads

ARA Spring 2025 recipients

Recipient

University

Research title

Xiaojing Liao

University of Illinois at Urbana–Champaign

Adversarial Misuse of Large Language Models in Digital Advertising: Benchmarking and Mitigation

Tianhao Wang

University of Virginia

Adversarial Misuse of Large Language Models in Digital Advertising: Benchmarking and Mitigation

AWS Agentic AI

ARA Spring 2025 recipients

Recipient

University

Research title

Faez Ahmed

Massachusetts Institute of Technology

AutoDA-Sim: A Multi-Agent Framework for Safe, Aesthetic, and Aerodynamic Vehicle Design

Fabio Anza

University of Maryland, Baltimore County

Physics Co-Pilot: An LLM-Orchestrated Scientific Assistant for Physics Research

Andrea Bajcsy

Carnegie Mellon University

Fine Grained Planning Evaluation for VLM Web Agents

Niranjan Balasubramanian

Stony Brook University

Efficient and Effective Long-Horizon Reasoning for Interactive LLM Agents

Andreea Bobu

Massachusetts Institute of Technology

Contextual Harm Mitigation and Automated Backtracking in Computer Use Agents

Joseph Campbell

Purdue University, West Lafayette

Open-World Probabilistic Theory of Mind

Cong Chen

Dartmouth College

Empowering Power Systems and Market Operations with Behavioral Generative Agents

Chunyang Chen

Technical University of Munich

Functional Bug-Aware Software Testing via Intelligent Computer Use Agents

Shay Cohen

University of Edinburgh

Diffusion-inspired chain-of-thought self-revision

Fernando De la Torre

Carnegie Mellon University

Fine Grained Planning Evaluation for VLM Web Agents

Sidong Feng

Monash University

Functional Bug-Aware Software Testing via Intelligent Computer Use Agents

James Fogarty

University of Washington, Seattle

Leveraging Multiple Representations in Multi-Agent Mobile App Interface Understanding and Task Execution

Surbhi Goel

University of Pennsylvania

Efficient and Safe Protocols for Collaborative Agentic AI

Nika Haghtalab

University of California, Berkeley

Multi-Agent AI Alignment

Irwin King

The Chinese University of Hong Kong

WebAGI: VLM-Driven Framework for Robust Web Automation and Planning in Agentic AI

Emma Lejeune

Boston University

Formidable yet Solvable: Scientific Computing Tasks for Agentic AI

Bang Liu

University of Montreal

Foundation Agents and Protocol for Collaborative Agentic AI

Harsha Madhyastha

University of Southern California

Improving the Efficiency of Web Agents

Michael Macy

Cornell University

Artificial Collective Intelligence: The Structure and Dynamics of LLM Communities

Radu Marculescu

University of Texas at Austin

Collaborative Continual Learning in Multimodal Multi-Agent Systems

Lianhui Qin

University of California, San Diego

ReaL-Agent: A Retrieval-and-Reasoning Agent for Deep, Cross-Modality Retrieval

Mahnam Saeednia

Delft University of Technology

Heterogeneous Multi-Agent Collaboration For Built-in Resilience

Maarten Sap

Carnegie Mellon University

OpenAgentSafety: Measuring and Mitigating Safety Harms of LLM-based AI Agent Interactions

Vitaly Shmatikov

Cornell University

Contextual Security for Multi-Agent Systems

Haim Sompolinsky

Harvard University

Lifelong learning in agentic AI through gated memory modules

John Torous

Harvard University

Interpreting Digital Phenotyping Data with LLM-Based Agentic Assistants for Mental Health Services

Jindong Wang

College of William & Mary

Structure Matters: Task-Optimized Topologies for LLM Agents

Xiaolong Wang

University of California, San Diego

Agentic World Representation

Zhi-Li Zhang

University of Minnesota, Twin Cities

NetGenius: Agentic AI for Next-Generation Wireless Network Autonomous Configurations and Intelligent Operations

Build on Trainium

ARA Spring 2025 recipients

Recipient

University

Research title

Saikat Dutta

Cornell University

VERA: Automated Testing for Improving the Reliability of Neuron Compiler Toolchain

Kuan Fang

Cornell University

Fast Adaptation of Multi-Modal Foundation Models for Robotic Perception and Control

Shizhong Han

Lieber Institute for Brain Development

Optimizing and scaling pretraining and preference-based fine-tuning of Large Chemical Models

Sitao Huang

University of California, Irvine

Automatic Kernel Synthesis and Tuning for AWS Trainium via Profile-Guided Graph Topology Optimization

Wataru Kameyama

Waseda University

Accelerating Vision-Language Autonomous Driving with AWS Trainium

Dong Li

University of California, Merced

Efficient Sparse Training with Adaptive Expert Parallelism on AWS Trainium

Xiaoxiao Li

University of British Columbia

Efficient MoE LLMs via Pruning and Matryoshka Quantization on AWS Trainium

Jiang Liu

Waseda University

Accelerating Vision-Language Autonomous Driving with AWS Trainium

Xiaoyi Lu

University of California, Merced

Accelerating Large Language and Reasoning Model Workloads with AWS Trainium

Satoshi Masuda

Tokyo City University

LLM for Software Modeling Brain in Multi Language

Andrew McCallum

University of Massachusetts, Amherst

Overcoming Fundamental Reasoning Limitations of LLMs by Always Thinking before Writing

Xupeng Miao

Purdue University, West Lafayette

Towards Communication-Efficient Distributed Training of Large Foundation Models by Dataflow-aware Optimizations

Michael Nagle

Lieber Institute for Brain Development

Optimizing and scaling pretraining and preference-based fine-tuning of Large Chemical Models

Jean-Christophe Nebel

Kingston University London

Efficient Architectures for Genomic Variant Interpretation: Language Models for Non-Coding DNA Variant Analysis

Farzana Rahman

Kingston University London

Efficient Architectures for Genomic Variant Interpretation: Language Models for Non-Coding DNA Variant Analysis

Rohan Sachdeva

University of California, Berkeley

Learning Host–Microbial Genetic Element Interactions with Genomic Language Models

Yanning Shen

University of California, Irvine

Automatic Kernel Synthesis and Tuning for AWS Trainium via Profile-Guided Graph Topology Optimization

Yun Song

University of California, Berkeley

Learning Host–Microbial Genetic Element Interactions with Genomic Language Models

Hoa Vo

Indiana University Bloomington

AI-Powered Travel Pattern Detection in VR for Occupant Behavior Analysis Using AWS Trainium

Minjia Zhang

University of Illinois Urbana-Champaign

Trainium-native MoE: Developing kernel and system optimizations for efficient and scalable MoE training

Think Big

ARA Spring 2025 recipients

Recipient

University

Research title

Tianlong Chen

University of North Carolina at Chapel Hill

Leveraging Molecular Dynamics to Empower Protein AI Models

William H. Lee

Yale School of Medicine

AI-powered prediction of ischemic stroke etiologies using multi-modal data

Piotr Sliz

Harvard Medical School

SBCloud – A Transformative Model for Scalable Structural Biology Research
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Member of Technical Staff, ML Infra, AGI
US, CA, San Francisco
Are you interested in a unique opportunity to advance the accuracy and efficiency of Artificial General Intelligence (AGI) systems? If so, you're at the right place! We are the AGI Autonomy organization, and we are looking for a driven and talented Member of Technical Staff to join us to build state-of-the art agents. Our lab is a small, talent-dense team with the resources and scale of Amazon. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We’re entering an exciting new era where agents can redefine what AI makes possible. We’d love for you to join our lab and build it from the ground up! Key job responsibilities * Design, build, and maintain the compute platform that powers all AI research at the SF AI Lab, managing large-scale GPU pools and ensuring optimal resource utilization * Partner directly with research scientists to understand experimental requirements and develop infrastructure solutions that accelerate research velocity * Implement and maintain robust security controls and hardening measures while enabling researcher productivity and flexibility * Modernize and scale existing infrastructure by converting manual deployments into reproducible Infrastructure as Code using AWS CDK * Optimize system performance across multiple GPU architectures, becoming an expert in extracting maximum computational efficiency * Design and implement monitoring, orchestration, and automation solutions for GPU workloads at scale * Ensure infrastructure is compliant with Amazon security standards while creatively solving for research-specific requirements * Collaborate with AWS teams to leverage and influence cloud services that support AI workloads * Build distributed systems infrastructure, including Kubernetes-based orchestration, to support multi-tenant research environments * Serve as the bridge between traditional systems engineering and ML infrastructure, bringing enterprise-grade reliability to research computing About the team This role is part of the foundational infrastructure team at the SF AI Lab, responsible for the platform that enables all research across the organization. Our team serves as the critical link between Amazon's enterprise infrastructure and the Lab's research needs. We are experts in performance optimization, systems architecture, and creative problem-solving—finding ways to push the boundaries of what's possible while maintaining security and reliability standards. We work closely with research scientists, understanding their experimental needs and translating them into robust, scalable infrastructure solutions. Our team has deep expertise in ML framework internals and GPU optimization, but we're also pragmatic systems engineers who build traditional infrastructure with enterprise-grade quality. We value engineers who can balance research velocity with operational excellence, who bring curiosity about ML while maintaining strong fundamentals in systems engineering. This is a small, high-impact team where your work directly enables breakthrough AI research. You'll have the opportunity to work with some of the most advanced AI infrastructure in the world while building the skills that define the future of ML systems engineering.
Senior Applied Scientist, Sponsored Products and Brands
US, NY, New York
About Sponsored Products and Brands The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About our team The Search Ranking and Interleaving (R&I) team within Sponsored Products and Brands is responsible for determining which ads to show and the quality of ads shown on the search page (e.g., relevance, personalized and contextualized ranking to improve shopper experience, where to place them, and how many ads to show on the search page. This helps shoppers discover new products while helping advertisers put their products in front of the right customers, aligning shoppers’, advertisers’, and Amazon’s interests. To do this, we apply a broad range of GenAI and ML techniques to continuously explore, learn, and optimize the ranking and allocation of ads on the search page. We are an interdisciplinary team with a focus on improving the SP experience in search by gaining a deep understanding of shopper pain points and developing new innovative solutions to address them. A day in the life As an Applied Scientist on this team, you will identify big opportunities for the team to make a direct impact on customers and the search experience. You will work closely with with search and retail partner teams, software engineers and product managers to build scalable real-time GenAI and ML solutions. You will have the opportunity to design, run, and analyze A/B experiments that improve the experience of millions of Amazon shoppers while driving quantifiable revenue impact while broadening your technical skillset. Key job responsibilities - Solve challenging science and business problems that balance the interests of advertisers, shoppers, and Amazon. - Drive end-to-end GenAI & Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Develop real-time machine learning algorithms to allocate billions of ads per day in advertising auctions. - Develop efficient algorithms for multi-objective optimization using deep learning methods to find operating points for the ad marketplace then evolve them - Research new and innovative machine learning approaches. - Recruit Scientists to the team and provide mentorship.
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