Project Grant 2338846
- This Project Grant award of $220,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research by Rensselaer Polytechnic Institute (RPI) to develop new stochastic optimization algorithms for solving nonconvex nonsmooth minimax problems. The project aims to create software packages that can improve the robustness of deep learning models, which are often vulnerable to adversarial attacks. The research will explore various...
- This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Program (CFDA #47.070) provides $525,000 in funding to The Johns Hopkins University to conduct collaborative research on "Post-Modern Min-Max Optimization Theory". The project aims to develop specialized theoretical frameworks and efficient algorithms tailored explicitly to min-max optimization, which underpins technologies ranging from generative AI...
- This $431,250 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research by Carnegie Mellon University to develop robust machine learning (ML) systems that can operate reliably in complex, real-world environments. The 5-year project (8/1/2025 - 7/31/2030) aims to bridge theoretical analysis and practical experimentation to address the brittleness of current ML models, which can fail unexpectedly under...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $250,000 to the Massachusetts Institute of Technology (MIT) to conduct research on developing corruption-robust online optimization and learning algorithms. The goal is to create resilient decision-making algorithms that can operate effectively in the face of security threats and adversarial attacks, supporting the resilience of critical...
- The National Science Foundation (NSF) awarded a $375,000 Project Grant under the Computer and Information Science and Engineering (CISE) Federal Grant Program to the University of California, Los Angeles (UCLA) on August 15, 2025. The grant will fund collaborative research to develop specialized theoretical frameworks and efficient algorithms tailored to min-max optimization, which underpins technologies ranging from generative artificial intelligence to large-scale reinforcement learning. The...
- This three-year Project Grant from the National Science Foundation's Division of Computing and Communication Foundations under the Computer and Information Science and Engineering program will support the development of new optimization approaches for machine learning problems. The $600,000 award to the University of Wisconsin-Madison beginning October 1, 2022 will advance optimization algorithms and analysis techniques for convex-concave minimax problems incorporating sparsity or regularity....
- This Project Grant award of $331,063, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to develop transformative methods for enhancing the resilience and reliability of machine learning (ML) systems in dynamic, real-world environments. The award will address three key challenges: 1) improving robustness generalization across data distributions, 2) ensuring robustness against multiple attacks simultaneously,...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, with CFDA Number 47.070, will support the development of new algorithmic techniques to achieve favorable tradeoffs between the robustness of machine learning (ML) algorithms and compression. The goal is to enable the deployment of robust ML algorithms on edge devices, such as phones, sensors, and IoT devices, which have computational and power limitations. The...
- This three-year National Science Foundation project grant of $300,000 will fund research to advance trustworthy machine learning through bi-level optimization. The grantee, the University of California, Santa Barbara, will develop new algorithms and computational methods to achieve robust and fair deep learning. Specifically, the project will create a bi-level optimization framework for robust learning, defenses against adversarial examples and distribution shifts, and a full-stack robustness...
- This $395,842 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research at Princeton University to improve the reliability of machine learning (ML) for critical networking functions. The project, titled "CAREER: Contextual Robustness for ML-Powered Network-Based Functions," aims to develop an open-source framework called "NetFortify" that helps researchers and engineers test and...
CAREER: STRUCTURED MINIMAX OPTIMIZATION: THEORY, ALGORITHMS, AND APPLICATIONS IN ROBUST LEARNING -THIS PROPOSAL SEEKS TO EXTEND THE MATHEMATICAL THEORY OF MINIMAX OPTIMIZATION, A VERY COMMONLY USED APPROACH THAT HAS PLAYED A PIVOTAL ROLE IN ADVANCING THE FIELDS OF INFORMATION THEORY, MACHINE LEARNING AND SIGNAL PROCESSING. NOTABLY, THERE HAS BEEN A RECENT SURGE OF INTEREST IN MINIMAX OPTIMIZATION DUE TO ITS CRITICAL RELEVANCE IN ARTIFICIAL INTELLIGENCE (AI), WHERE IT CAN BE USED TO MAKE DEEP NEURAL NETWORKS MORE RESILIENT AGAINST ADVERSARIAL DISTURBANCES IN THE UNDERLYING DISTRIBUTION OF DATA. WHILE THERE HAS BEEN RECENT PROGRESS IN ENHANCING THE THEORY OF MINIMAX OPTIMIZATION AND ITS ALGORITHMS, A NOTABLE GAP PERSISTS IN THE APPLICABILITY OF THIS THEORY TO REAL-WORLD AI SCENARIOS. EXISTING ALGORITHMS AND THEORY PRIMARILY FOCUS ON THE SO-CALLED CONVEX-CONCAVE OPTIMIZATION SETTING, WHILE CONTEMPORARY LEARNING APPLICATIONS FREQUENTLY ENTAIL MINIMAX PROBLEMS THAT DO NOT ADHERE TO THIS STRUCTURE AND ARE MORE COMPLEX. THIS PROJECT AIMS TO DEVELOP EFFICIENT METHODS FOR MINIMAX OPTIMIZATION FOR AI BY CAPITALIZING ON THE UNIQUE STRUCTURE OF THE AI PREDICTION FUNCTION. THIS INTERDISCIPLINARY PROJECT INTEGRATES RESEARCH FINDINGS INTO GRADUATE AND UNDERGRADUATE COURSES AND PROMOTES STEM INTEREST AMONG HIGH SCHOOL STUDENTS. THE OVERARCHING GOAL OF THIS PROJECT IS TO ADVANCE OPTIMIZATION THEORY AND ALGORITHMS FOR ROBUST MACHINE LEARNING MODEL TRAINING BY INVESTIGATING THEIR STRUCTURED MINIMAX OPTIMIZATION PROBLEMS. THE MINIMIZATION PROBLEMS IN ROBUST LEARNING ARE STRUCTURED BASED ON THE PREDICTION FUNCTION MODEL, AND WE WILL INVESTIGATE DIFFERENT TYPES OF MODELS, SUCH AS NEURAL NETWORKS. FOR EACH CONSIDERED PREDICTION FUNCTION, THE MINIMIZATION COMPONENT OF THE MINIMAX PROBLEM IS NONCONVEX; NONETHELESS, THEY EACH POSSESS A DISTINCT STRUCTURE THAT WE METICULOUSLY EXPLORE IN SEPARATE THRUSTS. MOREOVER, WITHIN EACH THRUST, WE STUDY DIFFERENT TYPES OF STRUCTURED INNER MAXIMIZATION PROBLEMS MOTIVATED BY THE THREE APPLICATIONS CLOSELY EXAMINED IN THIS PROJECT: DISTRIBUTIONALLY ROBUST LEARNING, ADVERSARIALLY ROBUST TRAINING, AND DISCRETE ROBUST LEARNING. THE DEVELOPED ALGORITHMS CAN SIGNIFICANTLY ADVANCE THESE APPLICATION AREAS IN TERMS OF COMPUTATIONAL COST AND ACCURACY. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $131.7k | 8/24/25 | ||
| Not listed | $126.5k | 7/28/25 | ||
| Not listed | $121.5k | 1/4/24 | ||
| Not listed | $0 | 1/4/24 |