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.

By Amazon Research Awards team

April 26, 2024

11 min read

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.

AI for Information Security

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

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

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

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

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

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

About the Author

Amazon Research Awards team