Dr. Ye’s research laboratory is centered around several key areas including intelligent electronics design, sensor data analytics, power electronics and power systems, electric machines, and advanced control algorithms. She has pioneered innovative approaches such as physics-guided data analytics and advanced control techniques to enhance the cyber-physical security, reliability, and resilience of critical systems. Leveraging her profound expertise in hardware and testbed development, her work spans across a wide range of applications including biomedical systems, smart grids, manufacturing systems, and electrified transportation. Currently, her research is sponsored by multiple funding agencies such as the National Science Foundation (NSF), the Department of Energy (DOE), the Department of Defense (DOD), and Southern Company.
Active Research Projects:
- PFI-RP: Advanced Anomaly Detection and Diagnostics for Electrical Devices and Networks: This project develops smart sensor technology for scalable anomaly detection and diagnostics in electrical devices and networks. It enhances the reliability and security of infrastructure ranging from homes and hospitals to industrial grids. In partnership with GE, RAFB, Siemens, and NEC Labs, the project also supports commercialization and workforce training in collaboration with LSAMP and REU programs.
- AMPS: Scalable Graph Models for Anomaly Detection in Large-Scale Smart Grids: This project develops real-time, data-driven graph-based algorithms for anomaly detection in complex, multi-source smart grids. It addresses key challenges such as scalability, noise robustness, and computational efficiency by modeling dynamic relationships across heterogeneous data. The work supports smart grid stability and security while fostering interdisciplinary student training across engineering, statistics, and computer science.
- Cyber-Physical Informatics and Security: This research portfolio develops advanced methods for anomaly, fault, and cyberattack detection by integrating data from both cyber and electrical signals across critical infrastructure, manufacturing systems, smart grids, and photovoltaic installations. The projects innovate in real-time data fusion, scalable sensing technologies, and multilevel cybersecurity solutions, enabling early detection, localization, and mitigation of threats in interconnected cyber-physical networks. Supported by NSF, DOE, and DoD funding, the work emphasizes interdisciplinary collaboration, diverse workforce training, and industry partnerships to enhance the security and reliability of modern electrified systems.
Completed Research Projects:
- SaTC: CORE: Medium: Cyber-threat Detection and Diagnosis in Manufacturing Systems through Cyber and Physical Data Analytics: This project aims to detect and diagnose cyber-threats in multistage manufacturing systems by fusing cyber and physical signals, since many attacks—particularly integrity attacks—are not fully observable in cyberspace alone. By leveraging cyber-physical data analytics and taint analysis, the research team is developing proactive tools to monitor, localize, and mitigate threats early, enhancing the security and reliability of software-defined manufacturing operations. The collaboration spans expertise in cybersecurity, manufacturing, and electrical drives, using virtual and hardware-in-the-loop testbeds for real-world validation.
- An Integrated Framework for Condition Monitoring and Fault Diagnosis of Electric Machine Drive Systems: This project develops a physics-guided, signature-based method for condition monitoring and fault diagnosis of electric machine networks using coordinated electrical waveform sensors. It advances fault detection by modeling fault propagation and network interactions, improving accuracy and efficiency over traditional single-machine sensor methods. The approach supports diverse applications including manufacturing, smart buildings, wind farms, and electrified transportation, while enhancing educational opportunities for students.
- Data-Driven Monitoring and Diagnosis of Industrial Machines via Electrical Waveform Auditing: This project developed an Electrical Waveform Fault and Attack Detection System (EWFADS) to monitor the health of industrial machines by analyzing power waveforms at the point of common coupling. It integrates advanced mathematical models, case studies, and machine learning algorithms to detect and diagnose both physical faults and cyberattacks. A real-time Hardware-in-the-Loop (HIL) testbed was constructed to validate the system under realistic industrial scenarios.
- Multilevel Cybersecurity for Photovoltaic Systems: This project develops a multilevel cybersecurity framework for photovoltaic (PV) systems by combining device-level (inverter) hardening with system-level intrusion detection and recovery strategies. The approach includes implementing security measures such as firmware protection, supply chain verification, and controller resilience at the inverter level, while coordinating broader system protections using data from solar farms. Solutions are validated using the NCREPT testbed and real-world solar farms operated by TPI, with oversight from industry leaders like GE and NREL.
- Low-Torque-Ripple Sensorless Control of Mutually Coupled Switched Reluctance Machines (MCSRMs): This project develops a novel control scheme for mutually coupled switched reluctance machines to reduce torque ripples and enable position sensorless control using a cost-effective six-switch converter. It addresses key challenges in rare-earth-free electric machines, improving reliability and efficiency for applications in electrified transportation, industry, and home appliances. The research integrates advanced nonlinear modeling with control design and supports education for future power engineering professionals.
- MRI: Acquisition of a Power-Hardware-in-the-Loop (PHIL) System to Enhance Research and Student Research Training in Engineering and Computer Science: This project acquires a state-of-the-art Power-Hardware-in-the-Loop system to advance integrated simulation and experimental testing for engineering and computer science research at two Hispanic-serving institutions. The system enables hands-on student training and cross-disciplinary collaborations, enhancing research in electrified vehicles, smart grids, prosthetic robotics, and structural analysis. It also supports diversity by empowering underrepresented students and junior female faculty while enriching curricula with cutting-edge technology.