Funded | 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.

DOE / Nuclear, Bayesian Optimization for Automatic Reactor Design Optimization

Sun, 01 Oct 2023 00:00:00 +0000

Overall Information

PI (10/2023–09/2026): Bayesian optimization for automatic reactor design optimization. Total \$1M (my effort 40%). Team: Andi Wang.

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.

DOT, "Knowledge-guided Automation for Integrity Management of Aging Pipelines (KAI-MAP) for Hydrogen Transport"

Thu, 01 Sep 2022 00:00:00 +0000

Overall Information

Co-PI on DOT KAI-MAP (09/2022–08/2025): knowledge-guided automation for integrity management of aging hydrogen pipelines for hydrogen transport. Total \$844,726 (my effort 30%). Team: Yongming Liu, Yiming Deng.

NIH R21, "Novel threat detection methodology to detect HIV outbreaks in Washington”

Thu, 01 Sep 2022 00:00:00 +0000

Overall Information

NIH R21 (09/2022–08/2024): statistical and machine learning methods for rapid detection of HIV transmission clusters and outbreaks using surveillance network data (team: Sarah Holte, Roxanne P. Kerani, Yajun Mei; Co-I).

NIH STTR Phase I, AIDen: An AI-empowered detection and diagnosis system for jaw lesions using CBCT

Thu, 01 Sep 2022 00:00:00 +0000

Overall Information

Dental CBCT is a 3D imaging modality widely adopted to help dentists detect and diagnose jaw lesions. Due to minimum information loss (compared to conventional 2D radiography) and low radiation exposure (compared to conventional CT), it has become the “go-to” radiographic technique in various dental fields. Gaps: Accompanying the clear benefits of dental CBCT is an overwhelming amount of 3D data presented to clinicians. Clinician-based CBCT interpretation suffers from low inter-/intra-observer agreement and low accuracy. AI/Deep Learning (DL) holds great promise to automate CBCT image analysis and provide objective, accurate detection and diagnosis capabilities to support clinical decision. However, limited research has been done due to unique and significant challenges: (1) Dental CBCT provides 3D images composed of a complicated mix of different oral structures/contents, preventing the direct use of existing general-purse DL algorithms for image segmentation and calling for new DL designs. (2) AI/DL is known to be data-hungry. It is very difficult to obtain a large number of accurately-annotated CBCT images to train DL due to complex oral anatomy and inevitable human errors, which calls for efficient strategies to reduce annotation effort for DL training. (3) Due to these challenges, the current software systems used to assist clinicians in dental CBCT interpretation do not provide advanced AI- based lesion detection and diagnosis capabilities, which makes this STTR project timely and important. We recently developed a DL algorithm that integrates unique oral anatomy into the DL design, namely “Anatomically-Constrained dense UNet (AC-UNet)”. In addition to improving accuracy, AC-UNet is also annotation-efficient as it is not only trained using CBCT images but also constrained by anatomical domain knowledge through novel mathematical encoding and posterior regularization-based optimization. Applied to a preliminary dataset of CBCTs with periapical lesions indicative of Apical Periodontitis (AP), AC-UNet achieved high accuracy in segmentation and lesion detection on CBCT images and outperformed state-of-the-art DL algorithms. Our long-term goal is to develop the first-ever AI-based software system called “AIDen” to perform automatic segmentation, lesion detection, and differential diagnosis based on dental CBCT for a variety of jaw lesions/diseases with high accuracy, reliability, and reproducibility. AIDen will assist clinicians in providing optimal treatment decision for each patient. Our Phase-I goal is to develop and test the feasibility of AIDen for lesion detection and differential diagnosis focusing on AP, a highly-prevalent jaw lesion/disease. Three aims are: (1) Optimize design: to develop an extension of AC-UNet to integrate a broader range of different types of oral-anatomical knowledge into the DL design; (2) Optimize training: to develop an Active Learning strategy to further improve annotation efficiency of AC-UNet training; (3) Clinical validation and preliminary assessment of diagnosis capability for clinical decision support. All aims will lay groundwork for Phase-II when an end-to-end AIDen system will be built and validated using multi-site datasets and address a variety of jaw lesions/diseases.The public health relevance of this project is to provide an Artificial Intelligence (AI)-based clinical decision support system, AIDen, to facilitate dental CBCT-based automatic segmentation, lesion detection, and differential diagnosis for a variety of jaw lesions/diseases with high accuracy, reliability, and reproducibility. AIDen will assist clinicians to provide optimal treatment decision for each individual patient. Our technology can be used by clinicians from a variety of dental fields such as endodontics, oral surgery, and oral medicine, and in a variety of settings including private practices, hospitals/clinics, medical/dental schools, and research institutes.

DOE SETO, Photovoltaic Plant Predictive Maintenance Optimization under Uncertainties Using Probabilistic Information Fusion

Wed, 01 Jul 2020 00:00:00 +0000

Overall Information

This project uses artificial intelligence and machine learning methods to develop algorithms that will optimize operation and maintenance of photovoltaic (PV) power plants by detecting and classifying anomalies, predicting failures, and scheduling maintenance activities. Predictive maintenance is important to maintain the long-term financial performance of solar PV plants and reduce downtime. Real-time monitoring data such as power output, temperature, and weather information can be used to identify the common fault class patterns using a hierarchical generative model and probabilistic information fusion framework in the sensor level and system level. This project will use the power plant operated at Arizona State University and Arizona Public Service as the case study to demonstrate the proposed technology for predictive maintenance.

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.

Procter & Gamble Company, Modeling Multi-Stage Manufacturing Processes and Related Problems

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

Overall Information

This project uses artificial intelligence and machine learning methods to develop algorithms for anomaly detection and quality prediciton in the manufacturing systems considering the heterogeneous data types in manufacturing systems (e.g., images, signals).

NASA ULI, Information Fusion for Real-Time National Air Transportation System Prognostics under Uncertainty

Tue, 01 Jan 2019 00:00:00 +0000

Overall Information

This is a NASA ULI project titled “Information Fusion for Real-Time National Air Transportation System Prognostics under Uncertainty” is funded by NASA. I serve as a Co-PI on the project to analyze the accident report data. Here is the website for this ULI project.

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: