This $398,786 Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports research at North Carolina State University to develop a privacy-preserving collaborative condition monitoring and decision-making methodology for distributed manufacturing systems. The project aims to enable multiple geographically distributed manufacturing facilities to collectively utilize their data to construct more effective monitoring and decision-making models, while...
This National Science Foundation (NSF) Engineering Research Initiation (ERI) Project Grant, awarded through CFDA 47.041 - Engineering, supports research to develop reliability modeling techniques for electronic packages in harsh environments. The $198,456 award, spanning June 2025 to May 2027, aims to create a new framework, theory, and methods to effectively and efficiently model the reliability of electronic packages under dynamic excitation such as extreme temperatures, humidity, mechanical...
This $281,589 federal Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) supports research to develop model-agnostic strategies for aligning artificial intelligence (AI) models with real-world operational goals in predictive maintenance for manufacturing systems. The project aims to: (i) design machine learning models that incorporate maintenance cost and operational constraints into predictive modeling, (ii) extend unit-level prognostics to...
This $174,371 National Science Foundation project grant supports research at Chapman University towards developing a personalized framework for real-time patient length of stay modeling and prediction. The framework aims to advance data fusion and time-to-event modeling techniques to enable proactive, data-driven hospital discharge scheduling. By integrating advanced tensor fusion and statistical learning approaches, the framework seeks to facilitate personalized length of stay predictions in...
This National Science Foundation Project Grant of $568,493 will support research at Northwestern University from April 1, 2022 to March 31, 2027 under the Engineering (47.041) federal grant program. The research will develop a comprehensive theoretical framework for analyzing rare catastrophic events driven by heavy-tailed distributions, which can model disparate phenomena like pandemics, blackouts, and financial crises. The framework will extend techniques in extreme value theory, optimization,...
This Project Grant award of $249,998 from the National Science Foundation Division of Electrical, Communications and Cyber Systems Engineering Directorate will fund research at Cornell University from September 2021 through August 2024. The research aims to advance co-design of prediction and control across data boundaries to improve efficiency, privacy, and markets. Specifically, the grantee will collaborate to develop techniques for coordinating predictive algorithms and controllers when...
This National Science Foundation (NSF) Faculty Early Career Development (CAREER) Project Grant, awarded under the NSF Engineering program (CFDA 47.041), aims to develop novel methodologies for understanding and enhancing the cyber-physical resilience of continuous critical manufacturing systems. The $540,362 award, effective October 1, 2024 through June 30, 2029, will support research objectives including: 1) creating generalizable tools for quantifying cyber-physical resilience, 2) rethinking...
This National Science Foundation (NSF) CAREER (Faculty Early Career Development) award, funded under the NSF Engineering program (CFDA 47.041), supports the development of a new approach to multifidelity scientific machine learning for engineering design. The $599,972 project will create machine learning models that can leverage both high-fidelity and low-fidelity computational simulations to enable more optimal and robust product designs across domains like space missions, biomedical devices,...
This $522,014 federal Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) supports research to develop new data-driven methods for correctly identifying interaction structures in complex dynamical networks. The research aims to address challenges in reconstructing network structures from finite, non-ideal data streams, such as those found in neural interactions, weather patterns, computing systems, and financial markets. The project will provide...
This $271,343 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports a collaborative research project to explore new methods for designing learning and inference systems that are robust to distributional uncertainty and data corruption. The project aims to advance research in areas such as statistical learning, optimization, control theory, network science,...