Ph.D. in Electronic and Computer Engineering
The Hong Kong University of Science and Technology
Portrait
Education
B.Eng. in Artificial Intelligence
School of Gifted Young · University of Science and Technology of China
Research area
Invasive Brain–Computer Interfaces
Google Scholar
Profile forthcoming
Selected Publications
View all →ECG-Based Seizure Prediction Using Progressive Feature Fusion Mamba
IEEE Transactions on Artificial Intelligence
ECG-Based Patient-Independent Epileptic Seizure Detection via Beat-to-Beat Dynamic Distribution Modeling and Label Uncertainty Suppression
Prepared for submission to Journal of Neural Engineering
Research Experience
Jan–Jun 2025 · Jan–Jun 2026
ECG-Based Patient-Independent Epileptic Seizure Detection via Beat-to-Beat Dynamic Distribution Modeling and Label Uncertainty Suppression
USTC · First author · Prepared for submission to Journal of Neural Engineering
- Developed a lightweight patient-independent ECG seizure-detection framework using beat-to-beat waveform and heart-rate dynamic-distribution features.
- Designed an EFCNN-based classifier with weighted cross-entropy to reduce the impact of uncertain seizure labels.
- Achieved 84.6% seizure-level true detection and a 0.138 h⁻¹ false-alarm rate under leave-one-subject-out evaluation, reducing false alarms by 94% over the reproduced state-of-the-art baseline.
Jul–Dec 2025
Decoding Gait of Spinal Cord Injury Mice Based on M1 Neural Activity
HKUST · Summer internship
- Decoded lower-limb gait in spinal-cord-injury mice from M1 neural activity using Kalman-filter and point-process methods.
- Processed multichannel neural recordings and video-based gait kinematics, including spike sorting, spike-train construction, neural–behavioral alignment, and gait normalization.
- Participated in animal behavioral training and learned experimental procedures for invasive brain–machine-interface research.
Jul–Dec 2024
ECG-Based Seizure Prediction Using Progressive Feature Fusion Mamba
USTC · Third author · IEEE Transactions on Artificial Intelligence
- Proposed a Progressive Feature Fusion Mamba (PFFMamba) network for patient-specific seizure prediction, with a progressive feature-fusion structure designed to improve Mamba's fusion of parallel features.
- Demonstrated performance beyond state-of-the-art methods on the USTC Epileptic ECG Database.
- Advised by Dr. Le Wu and Prof. Xun Chen, Department of Electronic Engineering and Information Science, USTC.
Publications
ECG-Based Seizure Prediction Using Progressive Feature Fusion Mamba
IEEE Transactions on Artificial Intelligence
ECG-Based Patient-Independent Epileptic Seizure Detection via Beat-to-Beat Dynamic Distribution Modeling and Label Uncertainty Suppression
Prepared for submission to Journal of Neural Engineering
Selected Awards
- Excellent Student Scholarship — Bronze (top 20%)
- Excellent Student Scholarship — Gold (top 3%)
- Artificial Intelligence Technology Talented Program Scholarship B (top 20% in the program)
- Rose Fund Public Affairs Scholarship
- Excellent Student Scholarship — Silver (top 10%)
- Artificial Intelligence Technology Talented Program Scholarship A (top 10% in the program)
- Rose Fund Public Affairs Scholarship
- Excellent Student Scholarship — Bronze (top 20%)
Leadership & Activities
Vice Minister, Office of the USTC Student Union
President, Student Union of the School of Gifted Young
Member, Student Union of the School of Gifted Young
Summer Admissions Volunteer
Volunteer Teacher in Yumin, Tacheng, Xinjiang