Project Grant 2239787
- This four-year, $622,992 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop methods for making machine learning models more interpretable and reliable. Specifically, researchers at the University of Virginia will investigate the mathematical foundations of deep neural networks, with a focus on geometry and topology, to better understand internal representations. Computational tools will be designed based on these...
- This $175,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program will fund the development of robust machine learning methods to address data disparities. The University of Michigan will receive funding from March 2022 through February 2024 to create predictive and causal machine learning tools for medical decision making that are reliable despite inaccuracies from underrepresented patient subgroups. Specifically, the university will...
- This Project Grant from the National Science Foundation Division of Information and Intelligent Systems provides $625,000 to Duke University to develop an interpretable artificial intelligence framework for improving care of critically ill patients. The framework incorporates novel matching techniques known as Almost-Matching-Exactly to analyze observational data from patient treatment and emulate a randomized controlled trial. By matching each treated patient to similar untreated patients,...
- This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) provides $600,000 to The Pennsylvania State University from October 1, 2022 to September 30, 2025. The university will develop interpretable machine learning methods based on deep neural networks from a source coding perspective. Researchers will draw an analogy between explaining complex prediction models and transmitting signals with limited channel capacity. The...
- This two-year, $250,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop techniques for improving the interpretability and robustness of deep neural networks. Specifically, the University of California, Santa Barbara will apply ideas from communication theory and neuroscience to actively shape the features extracted by individual layers of neural networks in addition to end-to-end training. By learning "matched...
- This $500,000 Project Grant award from the National Science Foundation's Division of Mathematical Sciences supports the development of novel deep learning techniques for interpretable survival analysis of complex longitudinal healthcare data. The project aims to create a unified deep learning model that can effectively analyze multi-modal data, such as text, images, and lab values, collected at irregular intervals to predict patient outcomes. Key objectives include providing a unified feature...
- This $659,678 NSF CAREER (Faculty Early Career Development) award, funded through the Engineering program (CFDA 47.041) and administered by the Division of Electrical, Communications and Cyber Systems, supports a five-year project (April 1, 2026 – March 31, 2031) at MIT to develop foundational technologies for trustworthy learning-enabled autonomous systems. The primary deliverables include new mathematical theory and efficient algorithms for constraint-satisfying learning, uncertainty-aware...
- This $160,118 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program will support The Johns Hopkins University in developing fast and accurate machine learning algorithms with interpretable mechanisms for learning from complex datasets. The project aims to close the theoretical and computational gap between data-independent and data-adaptive random partitioning methods in machine learning, by utilizing and expanding the toolkit of...
- This three-year $300,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to advance understanding of robustness in machine learning models. Specifically, the University of Maryland, College Park will research conditions under which adversarial attacks on deep networks can be detected and original data reconstructed. It will also study fundamental limits of robustness guarantees against poisoning attacks, especially with a...
- This $271,343 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research into developing robust machine learning and inference methods that can withstand data corruption and distribution shifts. The project aims to explore new techniques for structured learning, supervised learning, and reinforcement learning that are resilient to these challenges, with potential applications in healthcare,...
CAREER: INTERPRETABLE AND ROBUST MACHINE LEARNING MODELS: ANALYSIS AND ALGORITHMS -WHILE THE IMPACT OF MACHINE LEARNING CONTINUES TO INCREASE IN DIFFERENT AREAS---FROM RECOMMENDATION SYSTEMS TO ALGORITHMIC TRADING, AND FROM MEDICAL IMAGING DIAGNOSIS TO MOLECULAR BIOLOGY---SOME LIMITATIONS OF THESE INCREASINGLY COMPLEX MODELS REPRESENT IMPORTANT SHORTCOMINGS FOR THEIR SAFE AND RESPONSIBLE DEPLOYMENT. ONE OF THESE LIMITATIONS IS THE LACK OF INTERPRETABILITY OF THESE PREDICTORS, MAKING IT DIFFICULT TO FAITHFULLY DETERMINE THE ROLE OF THE MOST RELEVANT PORTIONS OF A GIVEN INPUT IN PRODUCING A CERTAIN OUTPUT. ANOTHER LIMITATION IS THEIR BRITTLENESS, AS THESE OUTPUTS CAN ALSO BE REMARKABLY UNSTABLE EVEN TO VERY SMALL PERTURBATIONS OF THE INPUTS. THESE PROBLEMS CAN COMPROMISE THE SAFE DEPLOYMENT OF MODERN MACHINE LEARNING TOOLS IN SENSITIVE DOMAINS, SUCH AS MEDICAL IMAGING. THIS PROJECT WILL DEVELOP FORMAL METHODS AND ALGORITHMS TO ALLEVIATE THESE SHORTCOMINGS. THIS CAREER PROJECT WILL DEVELOP A GENERAL FRAMEWORK TO INTERPRET COMPLEX PREDICTORS IN A ROBUST AND CERTIFIABLE MANNER. IN PARTICULAR, THIS PROJECT WILL FIRST DEFINE NEW NOTIONS OF LOCAL FEATURE IMPORTANCE AND DEVELOP CORRECT AND EFFICIENT METHODS TO ESTIMATE THEM. THESE DEFINITIONS WILL BE GIVEN IN TERMS OF LOCAL CONDITIONAL INDEPENDENCE TESTS WHILE MAKING MINIMAL ASSUMPTIONS ABOUT THE PREDICTION FUNCTIONS, AS WELL AS EXTENSIONS TO SEMANTICALLY IMPORTANT CONCEPTS. SECOND, THIS PROJECT WILL PROPOSE AND ANALYZE ALGORITHMS TO CERTIFY THE STABILITY OF PREDICTIVE MODELS LOCALLY ON MANIFOLDS, AS WELL AS GUARANTEE THE STABILITY AND ROBUSTNESS OF MODEL INTERPRETATIONS. THE METHODS DERIVED FROM THIS PROJECT WILL BE EVALUATED ON A SERIES OF MEDICAL IMAGING PROBLEMS THAT INCLUDE CHEST X-RAYS AND COMPUTED TOMOGRAPHY. IN ADDITION, THIS PROJECT WILL CARRY OUT A HOLISTIC EDUCATIONAL AND OUTREACH PROGRAM DEDICATED TO INCREASING THE REPRESENTATION OF MINORITY STUDENTS IN STEM, INCLUDING K-12 AND COMMUNITY OUTREACH THROUGH THE JOHNS HOPKINS CENTER FOR EDUCATIONAL OUTREACH, STRATEGIC UNDERGRADUATE AND GRADUATE RESEARCH PROJECTS, AND BROAD DISSEMINATION TO BOTH THE SCIENTIFIC COMMUNITY AND THE GENERAL PUBLIC, AMONG OTHER INITIATIVES. 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.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $115.1k | 8/24/25 | ||
| Not listed | $115.9k | 7/14/25 | ||
| Not listed | $219.2k | 1/19/23 |