NSF | Hao Yan

LEAP-HI, Sustainable Hydrogen Transport Pipeline Network with Learning-enabled Autonomous Risk Assessment Systems (LARAS)

Mon, 01 Sep 2025 00:00:00 +0000

Overall Information

Co-PI on LEAP-HI (09/2025–08/2029): sustainable hydrogen transport pipelines with learning-enabled autonomous risk assessment (LARAS). Total award \$2M (my effort 20%). Team: Wenlong Zhang, Yiming Deng, Yongming Liu, Hanna Breetz.

NSF, Multi-Agent Adaptive Data Collection for Automated Post-Disaster Rapid Damage Assessment

Fri, 01 Sep 2023 00:00:00 +0000

Overall Information

PI on collaborative NSF award (09/2023–08/2026): multi-agent adaptive data collection for automated post-disaster rapid damage assessment. Total \$550K (my effort 30%). Team: Mostafa Reisi, Mohammad Illbeigi.

NSF CMMI OE, Hybridizing Data and Model Driven Approaches for Proactive Production Control

Thu, 19 Mar 2020 00:00:00 +0000

Overall Information

This project will contribute to the national prosperity by investigating a framework for factory-level production control using real-time equipment-level sensing data. Rapid advances in sensor technology, computer-controlled processes, high-performance computing, and internet-of-things (IoT) have the potential to improve the productivity of U.S. manufacturing significantly. However, current production systems remain predominately retrospective and responsive to adverse events because real-time analysis has not been sufficiently integrated in proactive decision support. This award investigates an approach based on hybridizing data-driven statistical methods and with product flow models to address the key challenges in real-time sensing, performance prediction, and proactive control of production systems. The knowledge developed from this research will enhance the understanding of the fundamental principles governing manufacturing systems operations from both theoretical and practical perspectives. The research is integrated with an education plan to enhance education and outreach activities in the minority and underrepresented groups.

This project supports fundamental research to advance the state-of-the-art in prognostics, data fusion, and real-time production controls at both process and system levels in production environments. The research will investigate a new data-driven approach that combines Bayesian generative models, tensor data analytics, and data-fusion for prognostics to determine a process-level health condition index based on heterogeneous sensing data at different sampling rates. Process-specific health condition information will be integrated with system-level stochastic models for production performance prediction by synthesizing the input from sensor measurements and the output of process-level prediction. Finally, a data-driven sequential decision-making problem will be formulated to derive adaptive control actions that optimize the performance at both system and process levels in real-time. The developed methodology will be tested and validated using data from a small-scale university lab and collaborating industry partners, as well as open data sets published by the National Institute of Standards and Technology.

NSF DMS ATD: Collaborative Research: Adaptive and Rapid Spatial- Temporal Threat Detection over Networks

Tue, 07 Aug 2018 00:00:00 +0000

Overall Information

This project aims to develop innovative machine learning and statistical algorithms for detecting, preventing, and responding to threats over networks. Two concrete applications are monitoring the threat of multi-antibiotic-resistant (MDR) gonorrhea from a network of clinics across the United States and monitoring HIV transmission in clusters of patients. The research has impact in many other practical applications, including biosurveillance, engineering, homeland security, finance, and public health, where large-scale spatial-temporal data streams are collected with the aim of rapid detection and prevention of threats. The research aims to develop crucial scalable algorithms and methods to effectively and efficiently monitor, analyze, and optimize responses in these situations. In addition, the project will integrate research and education by infusing the research findings into the curriculum and by involving Ph.D. students in research.

This project aims to develop innovative algorithms for rapid threat detection by combining spatial-temporal models, ordinary differential equation (ODE) models with change-point detection, and multi-armed bandit and ensemble methods when monitoring large-scale spatial-temporal data over networks. In particular, efficient scalable algorithms are developed in three interrelated research tasks, including (1) rapid detection of threats by combining a “background + anomaly + noise” decomposition framework with sequential change-point detection; (2) predictive analytics of threats by applying multi-armed bandit algorithms and adaptive sampling in the changing environments to assess increasing risks at the population level; and (3) prescriptive analytics of threats by developing nested ensemble models based on calibrated ODE and data-driven spatial-temporal models so as to better assess the effects of prevention/intervention actions. Results of the project are expected to significantly advance the state of the art in spatial-temporal models, online learning, streaming data analysis, and large-scale inference. :Software:Funded:NSF: :Software:Funded:NSF: