105 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 105 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

Recipient

University

Research title

Murat Kocaoglu

Purdue University

Causal Anomaly Detection from Non-stationary Time-series in the Cloud

Hui Liu

Michigan State University

Harnessing the Power of Weakly-Supervised Graph Representation Learning for Cybersecurity

Xiaorui Liu

North Carolina State University

Harnessing the Power of Weakly-Supervised Graph Representation Learning for Cybersecurity

Thomas Pasquier

University of British Columbia

Building Robust Provenance-based Intrusion Detection

Michalis Polychronakis

Stony Brook University

SafeTrans: AI-assisted Transcompilation to Memory-safe Languages

Automated Reasoning

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

Recipient

University

Research title

Armin Biere

University of Freiburg

From Mavericks to Teamplayers: Fostering Solver Cooperation in Distributed SAT Solving

Victor Braberman

Universidad de Buenos Aires

Abstractions for Validating Distributed Protocol Reference Implementations

Varun Chandrasekaran

University of Illinois Urbana-Champaign

Automating Privacy Compliance

Maria Christakis

TU Wien

Testing Dafny for Unsoundness and Brittleness Bugs

Werner Dietl

University of Waterloo

Optional Type Systems for Model-Implementation Consistency

Alastair Donaldson

Imperial College London

Validating Compilers for the Dafny Verified Programming Language

Azadeh Farzan

University of Toronto

Better Predictability in Dynamic Data Race Detection

Sicun Gao

University Of California, San Diego

Proof Optimization and Generalization in dReal

Tobias Grosser

University Of Cambridge

Correct and High-Performance Domain-Specific Compilation with Lean and MLIR

Andrew Head

University Of Pennsylvania

TYCHE: An IDE for Property-Based Testing

Kihong Heo

Korea Advanced Institute Of Science and Technology - KAIST

Generative Translation Validation for JIT Compiler in the V8 JavaScript Engine

Frans Kaashoek

Massachusetts Institute of Technology

Flotilla: Compositional Formal Verification of Liveness of Distributed Systems Implementations

Baris Kasikci

University of Washington - Seattle

Privacy-Conscious Failure Reproduction for Root Cause Diagnosis in Large-Scale Distributed Systems

Laura Kovacs

TU Wien

QuAT: Quantifiers with Arithmetic Theories are Friends with Benefits

Shriram Krishnamurthi

Brown University

Paralegal: Scalable Tooling to Find Privacy Bugs in Application Code

Corina Pasareanu

Carnegie Mellon University

Proving the Absence of Timing Side Channels in Cryptographic Applications

Jean Pichon-Pharabod

Aarhus University

Validating Isolation of Virtual Machines in the Cloud

Benjamin Pierce

University Of Pennsylvania

TYCHE: An IDE for Property-Based Testing

Ruzica Piskac

Yale University

Democratizing the Law - Using LLMs and Automated Reasoning for Legal Reasoning

Malte Schwarzkopf

Brown University

Paralegal: Scalable Tooling to Find Privacy Bugs in Application Code

Peter Sewell

University Of Cambridge

The Foundations of Cloud Virtual-machine Isolation

Scott Shapiro

Yale University

Democratizing the Law - Using LLMs and Automated Reasoning for Legal Reasoning

Geoffrey Sutcliffe

University Of Miami

Automated Theorem Proving Community Infrastructure in the AWS Cloud

Joseph Tassarotti

New York University

Asynchronous Couplings for Probabilistic Relational Reasoning in Dafny

Sebastian Uchitel

Universidad de Buenos Aires

Abstractions for Validating Distributed Protocol Reference Implementations

Josef Urban

Czech Technical University

Learning Based Synthesis Meets Learning Guided Reasoning

Thomas Wies

New York University

Automating Privacy Compliance

Nickolai Zeldovich

Massachusetts Institute of Technology

Flotilla: 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

Recipient

University

Research title

Pulkit Agrawal

Massachusetts Institute Of Technology

Adapting Foundation Models without Finetuning

Niranjan Balasubramanian

Stony Brook University

An API Sandbox for Complex Tasks on Common Applications

Osbert Bastani

University Of Pennsylvania

Uncertainty Quantification for Trustworthy Language Generation

Matei Ciocarlie

Columbia University

Do You Speak EMG? Generative Pre-training on Electromyographic Signals for Controlling a Rehabilitation Robot after Stroke

Caiwen Ding

University of Connecticut

Graph of Thought: Boosting Logical Reasoning in Large Language Models

Yufei Ding

University Of California, San Diego

A Hollistic Compiler and Runtime System for Efficient and Scalable LLM Serving

Xinya Du

University Of Texas At Dallas

Process-guided Fine-tuning for Answering Complex Questions

Luciana Ferrer

University of Buenos Aires - CONICET

Efficient Adaptation of Generative Language Models through Unsupervised Calibration

Jakob Foerster

University Of Oxford

Compute-only Scaling of Large Language Models

Nikhil Garg

Cornell University

Recommendation systems in high-stakes settings

Georgia Gkioxari

California Institute Of Technology

Towards a 3D Foundation Model: Recognize and Reconstruct Anything

Tom Goldstein

University of Maryland

Building Safer Diffusion Models

Aditya Grover

University of California, Los Angeles

Personalizing Multimodal Generative Models via In-Context Preference Modeling

Albert Gu

Carnegie Mellon University

Scaling the Next Generation of Foundation Model Architectures

Mahdi S. Hosseini

Concordia University

Toward Auto-Populating Synoptic Reports in Diagnostic Pathology

Maliheh Izadi

Delft University Of Technology

Understanding and Regulating Memorization in Large Language Models for Code

Vijay Janapa Reddi

Harvard University

Benchmarking the Safety of Generative AI Models with Data-centric AI Challenges

Adel Javanmard

University of Southern California

Reliable AI for Generation of Medical Reports from MRI Scans

Jianbo Jiao

University Of Birmingham

PCo3D: Physically Plausible Controllable 3D Generative Models

Subbarao Kambhampati

Arizona State University

Understanding and Leveraging Planning, Reasoning & Self-Critiquing Capabilities of Large Language Models

Kangwook Lee

University Of Wisconsin–Madison

Information and Coding Theory-Based Framework for Prompt Engineering

Ales Leonardis

University Of Birmingham

PCo3D: Physically Plausible Controllable 3D Generative Models

Anqi Liu

Johns Hopkins University

(Multi-)Calibrated Active Learning under Subpopulation Shift

Lydia Liu

Princeton University

From Predictions to Positive Impact: Foundations of Responsible AI in Social Systems

Song Mei

University Of California, Berkeley

Mathematical Foundations and Physical Principles of Foundation Models and Generative AI

Pablo Piantanida

National Centre for Scientific Research (CNRS)

Efficient Adaptation of Generative Language Models through Unsupervised Calibration

Chara Podimata

Massachusetts Institute Of Technology

Responsible AI through User Incentive-Awareness

Bhiksha Raj

Carnegie Mellon University

Text and Speech Large Language Models

Christian Rupprecht

University Of Oxford

Viewset Diffusion for Probabilistic 3D Reconstruction

Olga Russakovsky

Princeton University

Diffusion models: Generative models beyond data generation

Vatsal Sharan

University Of Southern California

Debiasing ML-based Decision Making using Multicalibration

Abhinav Shrivastava

University Of Maryland

Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos

Rachee Singh

Cornell University

Accelerating collective communication for distributed ML

Vincent Sitzmann

Massachusetts Institute Of Technology

2D and 3D Animation via Image-Conditional Generative Flow Models

Justin Solomon

Massachusetts Institute Of Technology

Lightweight Algorithms for Generative AI

Mahdi Soltanolkotabi

University of Southern California

Reliable AI for Generation of Medical Reports from MRI Scans

Qian Tao

Delft University of Technology

Φ-Generative Medical Imaging by Physics and AI (PhAI)

Yapeng Tian

University Of Texas At Dallas

Integrating Visual Alignment and Text Interaction for Multi-modal Audio Content Generation

Sherry Tongshuang Wu

Carnegie Mellon University

Generating Deployable Models from Natural Language Instructions through Adaptive Data Curation

Florian Tramer

Eth Zurich

Can Technology Protect us from Generative AI?

Arie van Deursen

Delft University Of Technology

Understanding and Regulating Memorization in Large Language Models for Code

Andrea Vedaldi

University Of Oxford

Viewset Diffusion for Probabilistic 3D Reconstruction

Carl Vondrick

Columbia University

Viper: Visual Inference via Python Execution for Reasoning

Xiaolong Wang

University of California, San Diego

Generating Compositional 3D Scenes and Embodied Tasks with Large Language Models

Eric Wong

University Of Pennsylvania

Adversarial Manipulation of Prompting Interfaces

Saining Xie

New York University

Image Sculpting: Precise Image Generation and Editing with Interactive Geometry Control

Rex Ying

Yale University

Diff-H: Hyperbolic Text-to-Image Diffusion Generative Model

Minlan Yu

Harvard University

Troubleshooting Distributed Training Systems

Zhiru Zhang

Cornell University

A 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

Recipient

University

Research title

Christopher Brzuska

Aalto University

Secure Messaging: Updates Efficiency & Verification

Tevfik Bultan

University of California, Santa Barbara

Detecting and Quantifying Information Leakages in Crypto Libraries

Muhammed Esgin

Monash University

Practical Post-Quantum Oblivious Pseudorandom Functions Supporting Verifiability

Nadia Heninger

University of California, San Diego

Bringing Modern Security Guarantees to End-to-End Encrypted Cloud Storage

Tal Malkin

Columbia University

Cryptographic Techniques for Machine Learning

Peihan Miao

Brown University

Advancing Private Set Intersection for Wider Industrial Adoption

Virginia Smith

Carnegie Mellon University

Rethinking Watermark Embedding and Detection for LLMs

Ron Steinfeld

Monash University

Practical 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

Recipient

University

Research title

Lei Cao

University Of Arizona

SEED: Simple, Efficient, and Effective Data Management via Large Language Models

Natacha Crooks

University Of California, Berkeley

Mammoths Are Slow: The Overlooked Transactions of Graph Data

Samuel Madden

Massachusetts Institute Of Technology

SEED: Simple, Efficient, and Effective Data Management via Large Language Models

Manuel Rigger

National University Of Singapore

Democratizing Database Fuzzing

Kexin Rong

Georgia Institute Of Technology

Dynamic Data Layout Optimization with Worst-case Guarantees

Sustainability

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

Recipient

University

Research title

Kate Armstrong

New York Botanical Garden

VERDEX: remote sensing of plant biodiversity

Praveen Bollini

University Of Houston

Data-driven design and optimization of selective nanoporous catalysts for biofuel conversion

Brandon Bukowski

Johns Hopkins University

Data-driven design and optimization of selective nanoporous catalysts for biofuel conversion

Alan Edelman

Massachusetts Institute of Technology

Scientific Machine Learning with Application to Probabilistic Climate Forecasting and Sustainability

Kosa Goucher-Lambert

University of California, Berkeley

LCAssist: An Interactive System for Life-Cycle-Informed Sustainable Design Decision-Making

Vikram Iyer

University of Washington - Seattle

Data-Driven Sustainable Polymer Design for Circuits, Packaging, and Actuators

Can Li

Purdue University

Design and Analysis of Sustainable Supply Chains Using Optimization and Large Language Models

Damon Little

New York Botanical Garden

VERDEX: remote sensing of plant biodiversity

Aniruddh Vashisth

University of Washington - Seattle

Data-Driven Sustainable Polymer Design for Circuits, Packaging, and Actuators

Ming Xu

Tsinghua University

Advancing Sustainable Practices in the AI Era: Integrating Large Language Models for Automated Life Cycle Assessment Modeling

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