Yongxin Liu
- Title
- Assistant Professor
- Yongxin.Liu@erau.edu Email
- Department
- Mathematics Department
- College
- College of Arts & Sciences
Areas of Expertise
Artificial Intelligence, Cyber-Physical Systems
External Links
The views expressed on faculty and external web pages are those of the author and do not necessarily represent those of Embry-Riddle Aeronautical University.
Biography
Dr. Yongxin (Jack) Liu received his first Ph.D. with a focus on Big Data Analytics in Intelligent Transportation Systems from the South China University of Technology in 2018, and received his second Ph.D. in 2021 in Computer Science with a focus on the Mathematical assurance of deep neural network from Embry-Riddle Aeronautical University. His current research focuses on Explainable AI for aviation, Unmanned Aerial Systems, Smart Manufacturing and Internet of Things.
Dr. Liu is the creator of zero-bias deep learning, which is applied in explainable AI and anomaly detection. He's also the creator of the Channel Separation Incremental Learning algorithm, which harnesses the catastrophic forgetting issues in deep learning with mathematical assurance and applied in ADS-B signal identification systems. Other than that, his early work in unauthorized drone detection has resulted in two patents and was cited as first reference by the Department of Defense in August 2021. Dr. Liu has received 4 best papers awards from international conferences and the 2021 Harry Rowe Mimno award from IEEE Aerospace and Electronics Systems Society. Dr. Liu’s also has a strong profolio in Big Data Analytics in Transportations and has been the PI of the following projects sponsored by U.S. Department of Transportation (USDoT):
- Machine Learning for Dynamic Airspace Reconfiguration Under Emergency Situations
- Improving Air Mobility Under Emergency Situations
His recent research project is funded under USDoT Tier 1 UTC Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE). Dr. Liu’s research thrust in CYBER-CARE is Trustworthy AI in the NextGen Transportation Systems.
Dr.Liu looks forward to working together in continuing to help shape the cybersecurity, cyber resilience, and cyber safety of the global aerospace ecosystem.
Education
- Ph.D. - Doctor of Philosophy in Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University
- M. - Master in Engineering: Agricultural Electrification and Automation, South China Agricultural University
- B. - Bachelor in Engineering: Communicarion Engineering, South China Agricultural University
Publications
- Zero-Bias Deep Neural Network for Quickest RF Signal Surveillance Dahai Liu (2023)
- Class-Incremental Learning for Wireless Device Identification in IoT Houbing Song (2022)
- Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT Houbing Song (2022)
- Zero-Bias Deep Neural Network for Quickest RF Signal Surveillance Houbing Song (2022)
- Cross-Modality Transfer Learning for Image-Text Information Management Houbing Song (2022)
- Zero-bias Deep Learning Enabled Quickest Abnormal Event Detection in IoT Houbing Song (2021)
- Zero-Bias Deep Neural Network for Quickest RF Signal Surveillance Publications (2021)
- Bio-inspired routing for heterogeneous Unmanned Aircraft Systems (UAS) swarm networking Houbing Song (2021)
- Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices Thomas Yang (2021)
- Reinforcement Learning Optimized Throughput for 5G Enhanced Swarm UAS Networking Houbing Song (2021)
- Real-Time Machine Learning for Quickest Detection Doctoral Dissertations and Master's Theses (2021)
- Throughput Optimization in Heterogeneous Swarms of Unmanned Aircraft Systems for Advanced Aerial Mobility Houbing Song (2021)
- Formal Proofs of Orthogonality for Class-Incremental Learning for Wireless Device Identification in IoT Houbing Song (2021)
- Class-Incremental Learning for Wireless Device Identification in IoT Publications (2021)
- UAS Detection and Negation Houbing Song (2021)
- Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices Houbing Song (2021)
- Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT Publications (2021)
- UAS Detection and Negation Houbing Song (2021)
- Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges, and Future Trends Houbing Song (2021)
- Distant Domain Transfer Learning for Medical Imaging. Houbing Song (2021)
- Lightweight blockchain assisted secure routing of swarm UAS networking Houbing Song (2021)
- Extensive Throughput Enhancement For 5G Enabled UAV Swarm Networking Houbing Song (2021)
- Feature-based Distant Domain Transfer Learning Houbing Song (2020)
- 5G-enabled Optimal Bi-Throughput for UAS Swarm Networking Houbing Song (2020)
- UAS Detection and Negation Publications (2020)
- A Decade Survey of Transfer Learning (2010\u20132020) Houbing Song (2020)
- RF Fingerprint Measurement For Detecting Multiple Amateur Drones Based on STFT and Feature Reduction Houbing Song (2020)
- Tree-Based Airspace Capacity Estimation Houbing Song (2020)
- Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices Publications (2020)
- Beamforming-Constrained Swarm UAS Networking Routing Houbing Song (2020)
- Uncertainty Theory Based Reliability-Centric Cyber-Physical System Design Houbing Song (2019)
- Integration of SDR and UAS for Malicious Wi-Fi Hotspots Detection Houbing Song (2019)
- Uncertainty Theory Based Reliability-Centric Cyber-Physical System Design Publications (2019)
- Levering Mobile Cloud Computing for Mobile Big Data Analytics Houbing Song (2017)
Memberships and Credentials
IEEE Senior MemberAwards, Honors and Recognitions
2021 Harry Rowe Mimno Award, IEEE Aerospace and Electronic Systems Society
Best Paper Award, IEEE GLOBECOM 2021
Best Paper Award, WASA 2020
Best Paper Award, IEEE CBDCom 2020
Best Paper Award, IEEE CPSCom-2019