About Me

I am Yuanyuan Zhang (张源远), a Postdoctoral Associate at the SensorWeb Lab, University of Georgia, working with Prof. Wenzhan Song. I received my Ph.D. degree in Electrical and Electronic Engineering from the University of Liverpool (XJTLU joint program, Suzhou) in November 2025, advised by Prof. Rui Yang. Before that, I obtained my M.Sc. in Control and Optimization from Imperial College London under the supervision of Prof. Eric C. Kerrigan, and my B.Eng. (First-Class Honours) in Electrical and Electronic Engineering, also from the University of Liverpool.

My research lies at the intersection of device-free sensing, robust deep learning, and time-series modelling. I am particularly interested in millimeter-wave radar-based contactless vital sign monitoring, where signal sparsity, motion artifacts, and limited annotated data continue to pose fundamental challenges. To address them, I develop solutions across the full stack — sparse signal processing, multi-task optimization, transfer learning, and ODE-embedded deep models — with the long-term goal of bringing reliable, unobtrusive cardiac sensing into real clinical and home environments.

I have authored several first-author papers in IEEE Transactions on Mobile Computing, IEEE Transactions on Instrumentation and Measurement, and EMBC (Oral, Top 7%). I also serve as a reviewer for IEEE TIM, Neurocomputing, and IEEE WF-IoT.

You can reach me via email, or find more information from my Google Scholar, GitHub, LinkedIn, and ORCID. A full CV is available.

Research Interests

• Contactless vital sign monitoring with millimeter-wave radar
• Robust and transfer learning under limited / noisy data
• Multi-task optimization and gradient alignment
• Time-series forecasting (Mamba / state-space models)
• Sparse signal processing for biomedical applications

🔥 News

• 2026.02:  🏆 Awarded the Marie Skłodowska-Curie Actions (MSCA) Postdoctoral Fellowship, with the fellowship to commence in Summer 2027.
• 2026.01:   Started as a Postdoctoral Associate at the SensorWeb Lab, University of Georgia.
• 2025.11:  🎓 Successfully defended my Ph.D. thesis “Robust Cardiac Feature Monitoring based on Millimeter-Wave Radar“.
• 2025.10:  🎉 Paper “From High-SNR Radar Signal to ECG: A Transfer Learning Model with Cardio-Focusing Algorithm for Scenarios with Limited Data” accepted by IEEE Transactions on Mobile Computing.
• 2025.07:  🎙️ Recover from Horcrux selected for Oral Presentation (Top 7%) at EMBC 2025.
• 2025.04:  🎉 radarODE accepted by IEEE Transactions on Mobile Computing, and radarODE-MTL accepted by IEEE Transactions on Instrumentation and Measurement.

📝 Publications

Co-author papers are listed at the end of each subsection. Full list on Google Scholar.

Journal

1. Yuanyuan Zhang, Haocheng Zhao, Sijie Xiong, Rui Yang, Eng Gee Lim, Yutao Yue, “From High-SNR Radar Signal to ECG: A Transfer Learning Model with Cardio-Focusing Algorithm for Scenarios with Limited Data”, IEEE Transactions on Mobile Computing, Oct. 2025.

2. Yuanyuan Zhang, Runwei Guan, Lingxiao Li, Rui Yang, Yutao Yue, Eng Gee Lim, “radarODE: An ODE-Embedded Deep Learning Model for Contactless ECG Reconstruction from Millimeter-Wave Radar”, IEEE Transactions on Mobile Computing, Apr. 2025.

3. Yuanyuan Zhang, Rui Yang, Yutao Yue, Eng Gee Lim, “radarODE-MTL: A Multi-Task Learning Framework with Eccentric Gradient Alignment for Robust Radar-Based ECG Reconstruction”, IEEE Transactions on Instrumentation and Measurement, Apr. 2025.

4. Yuanyuan Zhang, Rui Yang, Yutao Yue, Eng Gee Lim, Zidong Wang, “An Overview of Algorithms for Contactless Cardiac Feature Extraction From Radar Signals: Advances and Challenges”, IEEE Transactions on Instrumentation and Measurement, Jul. 2023.

5. Sijie Xiong, Cheng Tang, Yuanyuan Zhang, Haoling Xiong, Youhao Xu, Atsushi Shimada, “CME-Mamba with Enhancing Nonlinear Dependencies for Time Series Forecasting”, Applied Soft Computing, Aug. 2025.

6. Sijie Xiong, Yuanyuan Zhang, Cheng Tang, Haoling Xiong, Yiding Li, Atsushi Shimada, “U-MA: A Unified Framework with Differential Mamba under Parallel U-Net Scheme for Time Series Forecasting”, Engineering Applications of Artificial Intelligence. (Under Review)

Conference

1. Yuanyuan Zhang, Sijie Xiong, Rui Yang, Eng Gee Lim, Yutao Yue, “Recover from Horcrux: A Spectrogram Augmentation Method for Cardiac Feature Monitoring from Radar Signal Components”, 47th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2025), Jul. 2025. (Oral, Top 7%)

💻 Projects

Open-source code and data accompanying my research. See GitHub for the full list.

TMC 2025

CFT-RFcardi  

A cardio-focusing + transfer-learning pipeline that learns ECG recovery from a small set of high-SNR radar signals and transfers to limited-data regimes. Released with a ready-to-run pre-processed dataset for quick validation.

Paper   ·   Code   ·   Dataset

TMC / TIM 2025

radarODE-MTL  

A multi-task learning framework with eccentric gradient alignment for robust radar-based ECG reconstruction. The repository implements both radarODE (IEEE TMC 2025) and radarODE-MTL (IEEE TIM 2025), decomposing long-term cardiac activity into individual cycles and leveraging an ODE decoder for noise robustness under body motion.

Paper (radarODE)   ·   Paper (radarODE-MTL)   ·   Code

EMBC 2025 · Oral

Horcrux  

A spectrogram-component augmentation method for cardiac feature monitoring from radar signals. Horcrux splits a spectrogram into time-consistent components and re-combines them to enlarge effective training data, plug-and-play for any radar-spectrogram pipeline.

Paper   ·   Code

Toolkit

GPR-B-scan-dataset-generator  

A synthetic B-scan dataset generation toolkit for training neural networks on ground-penetrating radar imagery, built on top of gprMax. Used in my undergraduate thesis on buried-object detection with CNNs.

Code

💼 Experience

• 2026.01 - Present, Postdoctoral Associate, SensorWeb Lab, University of Georgia, USA. Advisor: Prof. Wenzhan Song.
• 2024.07 - 2025.03, Research Assistant, HKUST (Guangzhou), China. Advisor: Prof. Yutao Yue.
  • Time-series forecasting for predicting daily hospital outpatient visits (consulting for Distinct HealthCare, Shenzhen).
• 2021.12 - 2024.11, Research Assistant, Deep Interdisciplinary Intelligence Lab, Institute of Deep Perception Technology (JITRI), Wuxi, China. Advisor: Prof. Yutao Yue.
  • Patent: Safety distance reminder system based on radar–camera fusion.
  • NSFC application: Pedestrian intention prediction for autonomous driving using multi-modality fusion.
  • Project: Next-generation radar–camera fusion for transportation with metamaterial and epistemic uncertainty.
• 2019.06 - 2019.08, Research Intern, NARI Group Corporation, Nanjing, China.
  • Communications system engineer; validated the PCS-9882 multi-port Ethernet switch (BERT, RFC 2544, multi-stream UDP).

📖 Education

• 2021.12 - 2025.11, Ph.D. in Electrical and Electronic Engineering, University of Liverpool (XJTLU joint program), Suzhou, China.
  • Thesis: Robust Cardiac Feature Monitoring based on Millimeter-Wave Radar.
  • Advisor: Prof. Rui Yang.
• 2020.10 - 2021.10, M.Sc. in Control and Optimization (EEE), Imperial College London, UK. GPA: 3.73/4.00.
  • Thesis: Derivative-free Multi-objective Optimization.
  • Advisor: Prof. Eric C. Kerrigan.
• 2018.09 - 2020.05, B.Eng. in Electrical and Electronic Engineering (Year 2 & 3), University of Liverpool, UK. GPA: 4.00/4.00.
  • Thesis: Detection and Classification of Buried Objects from GPR Image Using CNN.

🛠️ Service & Skills

Professional Service  ·  Reviewer for IEEE Transactions on Instrumentation and Measurement, Neurocomputing, IEEE WF-IoT 2025.

Programming  ·  C / C++, Python, PyTorch, MATLAB, Julia, GPRmax, LaTeX.

Languages  ·  Chinese (Native), English (Fluent).

Volunteer  ·  Primary School Teacher, AIESEC Overseas Volunteer Program, Colombo, Sri Lanka (Jul. 2017).

Off the clock  ·  Swimming, fingerstyle guitar, classical music.