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75 Amazon Research Award recipients announced

Awardees, who represent 46 universities in 10 countries, have access to Amazon public datasets, along with AWS AI/ML services and tools.

By Amazon Research Awards team
June 3, 2025
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 75 award recipients who represent 46 universities in 10 countries.

This announcement includes awards funded under five call for proposals during the fall 2024 cycle: AI for Information Security, Automated Reasoning, AWS AI, AWS Cryptography, 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.

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“Automated Reasoning is an important area of research for Amazon, with potential applications across various features and applications to help improve security, reliability, and performance for our customers. Through the ARA program, we collaborate with leading academic researchers to explore challenges in this field,” said Robert Jones, senior principal scientist with the Cloud Automated Reasoning Group. “We were again impressed by the exceptional response to our Automated Reasoning call for proposals this year, receiving numerous high-quality submissions. Congratulations to the recipients! We're excited to support their work and partner with them as they develop new science and technology in this important area.”

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“At Amazon, we believe that solving the world's toughest sustainability challenges benefits from both breakthrough scientific research and open and bold collaboration. Through programs like the Amazon Research Awards program, we aim to support academic research that could contribute to our understanding of these complex issues,” said Kommy Weldemariam, Director of Science and Innovation Sustainability. “The selected proposals represent innovative projects that we hope will help advance knowledge in this field, potentially benefiting customers, communities, and the environment.”

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 2024 cycle call-for-proposal recipients, sorted by research area.

AI for Information Security

ARA-AIInfoSecurity-1200x750.png

Recipient

University

Research title

Christopher Amato

Northeastern University

Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms

Bernd Bischl

Ludwig Maximilian University of Munich

Improving Generative and Foundation Models Reliability via Uncertainty-awareness

Shiqing Ma

University Of Massachusetts Amherst

LLM and Domain Adaptation for Attack Detection

Alina Oprea

Northeastern University

Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms

Roberto Perdisci

University of Georgia

ContextADBench: A Comprehensive Benchmark Suite for Contextual Anomaly Detection

Automated Reasoning

ARA-AutomatedReasoning-1200x750.png

AWS AI

Recipient

University

Research title

Nada Amin

Harvard University

LLM-Augmented Semi-Automated Proofs for Interactive Verification

Suguman Bansal

Georgia Institute of Technology

Certified Inductive Generalization in Reinforcement Learning

Ioana Boureanu

University of Surrey

Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems

Omar Haider Chowdhury

Stony Brook University

Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege

Stefan Ciobaca

Alexandru Ioan Cuza University

An Interactive Proof Mode for Dafny

João Ferreira

INESC-ID

Polyglot Automated Program Repair for Infrastructure as Code

Aymeric Fromherz

Inria

Extensible Proof Automation for Rust Program Verification in Lean using Aeneas

Sicun Gao

University Of California, San Diego

Monte Carlo Trees with Conflict Models for Proof Search

Mirco Giacobbe

University of Birmingham

Neural Software Verification

Tobias Grosser

University of Cambridge

Synthesis-based Symbolic BitVector Simplification for Lean

Ronghui Gu

Columbia University

Scaling Formal Verification of Security Properties for Unmodified System Software

Alexey Ignatiev

Monash University

Huub: Next-Gen Lazy Clause Generation

Kenneth McMillan

University of Texas At Austin

Synthesis of Auxiliary Variables and Invariants for Distributed Protocol Verification

Alexandra Mendes

University of Porto

Overcoming Barriers to the Adoption of Verification-Aware Languages

Raphaël Monat

University of Lille and Inria

Resource‐Aware Conservative Static Analysis

Jason Nieh

Columbia University

Scaling Formal Verification of Security Properties for Unmodified System Software

Rohan Padhye

Carnegie Mellon University

Automated Synthesis and Evaluation of Property-Based Tests

Nadia Polikarpova

University Of California, San Diego

Discovering and Proving Critical System Properties with LLMs

Fortunat Rajaona

University of Surrey

Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems

Subhajit Roy

Indian Institute of Technology Kanpur

Theorem Proving Modulo LLM

Gagandeep Singh

University of Illinois At Urbana–Champaign

Trustworthy LLM Systems using Formal Contracts

Scott Stoller

Stony Brook University

Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege

Peter Stuckey

Monash University

Huub: Next-Gen Lazy Clause Generation

Yulei Sui

University of New South Wales

Path-Sensitive Typestate Analysis through Sparse Abstract Execution

Nikos Vasilakis

Brown University

Semantics-Driven Static Analysis for the Unix/Linux Shell

Ping Wang

Stevens Institute of Technology

Leveraging Large Language Models for Reasoning Augmented Searching on Domain-specific NoSQL Database

John Wawrzynek

University of California, Berkeley

GPU-Accelerated High-Throughput SAT Sampling
ARA-AWSAI-1200x750.png

Recipient

University

Research title

Panagiotis Adamopoulos

Emory University

Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations

Vikram Adve

University of Illinois at Urbana–Champaign

Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models

Frances Arnold

California Institute of Technology

Closed-loop Generative Machine Learning for De Novo Enzyme Discovery and Optimization

Yonatan Bisk

Carnegie Mellon University

Useful, Safe, and Robust Multiturn Interactions with LLMs

Shiyu Chang

University of California, Santa Barbara

Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing

Yuxin Chen

University of Pennsylvania

Provable Acceleration of Diffusion Models for Modern Generative AI

Tianlong Chen

University of North Carolina at Chapel Hill

Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing

Mingyu Ding

University of North Carolina at Chapel Hill

Aligning Long Videos and Language as Long-Horizon World Models

Nikhil Garg

Cornell University

Market Design for Responsible Multi-agent LLMs

Jessica Hullman

Northwestern University

Human-Aligned Uncertainty Quantification in High Dimensions

Christopher Jermaine

Rice University

Fast, Trusted AI Using the EINSUMMABLE Compiler

Yunzhu Li

Columbia University

Physics-Informed Foundation Models Through Embodied Interactions

Pattie Maes

Massachusetts Institute of Technology

Understanding How LLM Agents Deviate from Human Choices

Sasa Misailovic

University of Illinois at Urbana–Champaign

Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models

Kristina Monakhova

Cornell University

Trustworthy extreme imaging for science using interpretable uncertainty quantification

Todd Mowry

Carnegie Mellon University

Efficient LLM Serving on Trainium via Kernel Generation

Min-hwan Oh

Seoul National University

Mutually Beneficial Interplay Between Selection Fairness and Context Diversity in Contextual Bandits

Patrick Rebeschini

University of Oxford

Optimal Regularization for LLM Alignment

Jose Renau

University of California, Santa Cruz

Verification Constrained Hardware Optimization using Intelligent Design Agentic Programming

Vilma Todri

Emory University

Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations

Aravindan Vijayaraghavan

Northwestern University

Human-Aligned Uncertainty Quantification in High Dimensions

Wei Yang

University of Texas at Dallas

Optimizing RISC-V Compilers with RISC-LLM and Syntax Parsing

Huaxiu Yao

University of North Carolina at Chapel Hill

Aligning Long Videos and Language as Long-Horizon World Models

Amy Zhang

University of Washington

Tools for Governing AI Agent Autonomy

Ruqi Zhang

Purdue University

Efficient Test-time Alignment for Large Language Models and Large Multimodal Models

Zheng Zhang

Rutgers University-New Brunswick

AlphaQC: An AI-powered Quantum Circuit Optimizer and Denoiser

AWS Cryptography

ARA-AWSCryptoPrivacy-1200x750.png

Recipient

University

Research title

Alexandra Boldyreva

Georgia Institute of Technology

Quantifying Information Leakage in Searchable Encryption Protocols

Maria Eichlseder

Graz University of Technology, Austria

SALAD – Systematic Analysis of Lightweight Ascon-based Designs

Venkatesan Guruswami

University of California, Berkeley

Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing

Joseph Jaeger

Georgia Institute of Technology

Analyzing Chat Encryption for Group Messaging

Aayush Jain

Carnegie Mellon

Large Scale Multiparty Silent Preprocessing for MPC from LPN

Huijia Lin

University of Washington

Large Scale Multiparty Silent Preprocessing for MPC from LPN

Hamed Nemati

KTH Royal Institute of Technology

Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary

Karl Palmskog

KTH Royal Institute of Technology

Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary

Chris Peikert

University of Michigan, Ann Arbor

Practical Third-Generation FHE and Bootstrapping

Dimitrios Skarlatos

Carnegie Mellon University

Scale-Out FHE LLMs on GPUs

Vinod Vaikuntanathan

Massachusetts Institute of Technology

Can Quantum Computers (Really) Factor?

Daniel Wichs

Northeastern University

Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing

David Wu

University Of Texas At Austin

Fast Private Information Retrieval and More using Homomorphic Encryption

Sustainability

ARA-Sustainability-1200x750.png

Recipient

University

Research title

Meeyoung Cha

Max Planck Institute

Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring

Jingrui He

University of Illinois at Urbana–Champaign

Foundation Model Enabled Earth’s Ecosystem Monitoring

Pedro Lopes

University of Chicago

AI-powered Tools that Enable Engineers to Make & Re-make Sustainable Hardware

Cheng Yaw Low

Max Planck Institute

Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring
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