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Fall 2025 Amazon Research Awards recipients announced

Awardees represent more than 49 universities in 11 countries. Recipients have access to Amazon public datasets, along with AWS AI/ML services and tools.

By Amazon Research Awards team
May 27, 2026
9 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 70 award recipients who represent 49 universities in 11 countries.

This announcement includes awards funded under 6 calls for proposals during the fall 2025 cycle: AI for Information Security, Agentic AI , Automated Reasoning, AWS Cryptography, Cybersecurity and Anti-Abuse Technologies, 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 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.

"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, 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."

“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.”

ARA funds proposals throughout the year in a variety of research areas. Applicants are encouraged to visit our 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 2025 cycle call-for-proposal recipients, sorted by research

AI for Information Security

Recipient

University

Research title

Peng Gao

Virginia Polytechnic Institute and State University

CortexCTI: A Unified Threat Intelligence Engine for Knowledge-Driven Cloud Threat Detection and Response

Guofei Gu

Texas A&M University

New Benchmark and Defense on Prompt Injection in Agentic AI Systems

Xiyang Hu

Arizona State University

Securing Agentic AI: From Local Detection to Global Assurance

Adriana Sejfia

The University of Edinburgh

Exploit-driven AI Agents for vulnerability detection verification

Yue Zhao

University of Southern California

Securing Agentic AI: From Local Detection to Global Assurance

Automated Reasoning

Recipient

University

Research title

Jonathan Aldrich

Carnegie Mellon University

A Visual Debugger for Program Verification

Dalal Alrajeh

Imperial College London

SOLAR: Symbolic Learning for Automated Requirements Consistency

Maria Paola Bonacina

University of Verona

New Data Structure Theories and Quantifiers in CDSAT

Jason Cong

University of California Los Angeles

Breaking the Parallelism Limit with SAT-solving Accelerators

Lucas Cordeiro

The University of Manchester

Combining Formal Methods with Large Language Models in ESBMC: Enabling Automated Program Verification through AI/ML

Werner Dietl

University of Waterloo

Strata-Sphere: Expressive Type Systems and Language Formalizations

Katalin Fazekas

TU Wien

PASSAT: Improved Passing of Assertion Stacks to SAT in Incremental SMT Solvers

Sicun Gao

University of California San Diego

Evaluating and Improving Quantitative Reasoning in LLM Agents Using Sandbox Coding Tasks and Formal Tools

Milos Gligoric

The University of Texas at Austin

Documenting and Recommending Tactics in HOL Light

Ronghui Gu

Columbia University in the City of New York

Scaling Formal Verification of Security Properties for Unmodified System Software

Tyler Josephson

University of Maryland Baltimore County

Autoformalization for Scientific Computing in Lean

Junyi Jessy Li

The University of Texas at Austin

Documenting and Recommending Tactics in HOL Light

Xiaorui Liu

North Carolina State University

Neurosymbolic LLM Reasoning with Symbolical Soundness and Logical Consistency

Azalea Raad

Imperial College London

Soteria in Lean: Mechanising the Next Generation of Symbolic Execution Tools

Dominik Schreiber

Karlsruhe Institute of Technology

Resource-Efficient Flexible SAT Solving in HPC and Cloud Environments

Ilya Sergey

National University of Singapore

Linear Types for a Foundational Multi-Modal Program Verifier

Peter Sewell

University of Cambridge

Gradual Lightweight Methods for High-Assurance Cloud Infrastructure

Paulo Shakarian

Syracuse University

Non-Markovian Agentic Meta-Reasoning

Armando Solar-Lezama

Massachusetts Institute of Technology

Synthesizing Library Models for Static Analysis via LLMs and Conformance Testing

Salil Vadhan

Harvard University

Translating Formal Proofs of Differential Privacy via LLMs

Nickolai Zeldovich

Massachusetts Institute of Technology

Verifying Rust distributed system implementations using monotonic ownership state machines in Verus

Xuezhou Zhang

Boston University

Auto-Formalization and Informalization through Two-Stage Reinforcement Learning

Tianyi Zhang

Purdue University

Scaling Interprocedural Data-Flow Analysis with LLMs

AWS Agentic AI

Recipient

University

Research title

Raman Arora

Johns Hopkins University

Multi-Party Differential Privacy: Unlocking Enterprise Agentic AI

Fanglin Che

Worcester Polytechnic Institute

Autonomous Catalyst Design with Agentic AI for Hydrogen Production

Muhao Chen

University of California Davis

FlowGuard: Evolutionary Red-Teaming for Safe Multimodal Web Agents

Ioannis Demertzis

University of California Santa Cruz

CAMEO: Confidential Agentic Multi-component Enclave Orchestration

Caiwen Ding

University of Minnesota Twin Cities

End-to-End Agentic AI for Scalable Chiplet Design with Extreme Parallelism and Heterogeneity

Ariel Felner

Ben-Gurion University of the Negev

Multi-Agent Pathfinding with Unassigned Agents

Zhaomiao Guo

The University of Texas at Austin

From Observation to Intervention: Counterfactual Multi-Agent World Models for Autonomous Driving

Jiangen He

The University of Tennessee-Knoxville

Beyond Walls of Text: Building UI-Native LLM Agents as the Next Gateway to the Internet

Fan Lai

University of Illinois at Urbana-Champaign

Reinforcing Coordination: Streaming, Exploration, and Distillation for Long-Horizon Agent Learning

Ziyang Li

Johns Hopkins University

A Protocol Stack for Resource-Bound Multi-Agent AI

Henry Liu

University of Michigan

Automating Large Scale Deployment of Infrastructure-based Safety Critical Event Detection with Agentic AI

Bryan Low Kian Hsang

National University of Singapore

Self-Configurable Agentic Learning via Co-optimization

Chinmay Maheshwari

Johns Hopkins University

Markov Near-Potential Function Based MARL Training for Mixed Cooperative–Competitive Agentic AI

Arash Noshadravan

Texas A&M University

A Retrieval-Augmented Dual-Attention Vision Framework for Standards-Aligned Infrastructure Inspection

Muhammad Shafique

New York University Abu Dhabi

AVAAS – Automated Vulnerability Analysis Through Advanced Agentic Systems

Roni Stern

Ben-Gurion University of the Negev

Multi-Agent Pathfinding with Unassigned Agents

Zhengzhong Tu

Texas A&M University

FlowGuard: Evolutionary Red-Teaming for Safe Multimodal Web Agents

Lu Wang

University of Michigan

Benchmarking and Monitoring Multi-Agent Scheming

Yuke Wang

Rice University

Empowering Multimodal AI Agents with Continuous Learning

Hamed Zamani

University of Massachusetts Amherst

A Framework for Proactive and Collaborative AI Agents

Yang Zhao

University of Minnesota Twin Cities

End-to-End Agentic AI for Scalable Chiplet Design with Extreme Parallelism and Heterogeneity

Victor Zhong

University of Waterloo

KNOWLEDGESTORE: A Dynamic Hierarchical Memory for Scalable, Enterprise-Ready AI Agents on AWS

AWS Cryptography

Recipient

University

Research title

Sri AravindaKrishnan Thyagarajan

The University of Sydney

Efficient Robust Post-Quantum Distributed Key Generation and Threshold Signatures

Daniel J. Bernstein

University of Illinois at Chicago

Formally verified symmetric cryptography

Jeremiah Blocki

Purdue University

Stronger Memory Hard Functions to Protect Passwords against Brute Force Attacks

Geoffroy Couteau

Paris Cité University

Pseudorandom Correlations for Threshold Cryptography

Yevgeniy Dodis

New York University

Machine Unlearning and Computational Assumptions for AI

Zhengzhong Jin

Northeastern University - United States of America

Practical Watermarking for LLMs via Pseduorandom Codes

Yael Kalai

Massachusetts Institute of Technology

Enhancing AI Safety Using Cryptography

John Liagouris

Boston University

Pushing secure MPC beyond niche applications

Rafail Ostrovsky

University of California Los Angeles

Towards Low-Latency Maliciously Secure MPC for LLMs

Rachel Player

Royal Holloway - University of London

New Approaches for the Linear Transform in BFV/BGV

Elaine Shi

Carnegie Mellon University

Practical Secure Computation At Scale

Akshayaram Srinivasan

University of Toronto

Simultaneous-Message and Succinct Secure Computation

Douglas Stebila

University of Waterloo

FantASM: Fast, Auditable, and Neat Assembly

Ni Trieu

Arizona State University

Fuzzy Secure Computation for Real-World Noisy Data

Xiao Wang

Northwestern University - United States of America

From Signing to Garbling: Exploring the Spectrum of Post-Quantum Primitives

Mark Zhandry

Stanford University

Algorithms for Post-Quantum Cryptography

Jiaheng Zhang

National University of Singapore

Practical Watermarking for LLMs via Pseduorandom Codes

Vassilis Zikas

Georgia Institute of Technology

Fuzzy Secure Computation for Real-World Noisy Data

Cybersecurity and Anti-Abuse Technologies

Recipient

University

Research title

Geoffrey Voelker

University of California San Diego

Detecting Anti-detect Browsers at Scale

Devices Sustainability

Recipient

University

Research title

Udit Gupta

Cornell University

Agent-Driven Life Cycle Carbon Optimization for Sustainable Edge Devices

Adriana Schulz

Brown University

Integrating Sustainability Reasoning into Early-Stage Electronics Design
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Member of Technical Staff, Frontier AI & Robotics (FAR)
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As a Member of Technical Staff, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
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