M.Sc. Computer Science & Technology @ Southwest University of Science and Technology
I work at the intersection of artificial intelligence, biomedical computing, and software engineering, with a growing focus on robust and explainable AI for healthcare. My research spans medical images, ECG and biomedical signals, deep learning, and computer vision, supported by experience building practical data and web systems.
Website · Google Scholar · ResearchGate · ORCID · LinkedIn · GitHub
- Pursuing an M.Sc. in Computer Science & Technology at Southwest University of Science and Technology in Mianyang, China.
- Developing a research direction in Biomedical AI, particularly robust learning for healthcare data.
- Working with deep learning, medical images, ECG and biomedical signals, computer vision, and applied machine learning.
- Bringing a software engineering background across Python, Django, React, APIs, and data-driven applications.
- Open to research collaboration and the development of practical, reproducible AI systems.
Biomedical AI · Medical Image Analysis · ECG & Biomedical Signals · Deep Learning
Computer Vision · Explainable AI · Robust Machine Learning · Applied Machine Learning
Current research on a CRNN-based diagnostic framework for mobile, scanned, and generated ECGs. The work focuses on artifact robustness, signal-quality assessment, segment-to-signal inference, multi-dataset evaluation, external generalization, and explainability.
Research areas: CRNN · ECG classification · Biomedical signal analysis · Robustness · Explainability
Co-authored an IEEE conference paper on deep-learning-based predictive and diagnostic analysis of retinal fundus images using imaging biomarkers.
Research areas: Biomedical AI · Fundus image analysis · Deep learning · Computer vision · Medical imaging
Applying computer vision and deep learning to rice disease image classification as an agricultural AI research project.
Research areas: Computer vision · Deep learning · Image classification · Agricultural AI
Co-authored research on machine-learning-based intrusion detection for nuclear power plant control networks.
Research areas: Intrusion detection · Machine learning · Critical infrastructure · Nuclear cybersecurity
Paper: A Machine Learning-Based Intrusion Detection System for Nuclear Power Plant Control Networks
- Deep Learning–Empowered Predictive and Diagnostic Analysis of Diabetic Retinopathy Using Fundus Imaging Biomarkers. 2026 Second International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI), IEEE, 2026. DOI:
10.1109/ICMSCI67830.2026.11469748. - A Machine Learning-Based Intrusion Detection System for Nuclear Power Plant Control Networks. Global Journal of Scientific Research, 13(11), 1235–1249, 2025.
A full-stack website platform with a localized React frontend and a separate content-management backend. The backend provides public content APIs, administrative workflows, and contact-form delivery.
Stack: React · TypeScript · Vite · Tailwind CSS · Next.js · Payload CMS · MongoDB · REST APIs
A componentized, multi-page service application with localized content, service and location routes, lead forms, and reusable interface sections.
Stack: React · TypeScript · Vite · Tailwind CSS · React Router · i18next
A React application for structured Chinese-language practice with client-side navigation and a focused learning interface.
Stack: React · JavaScript · Vite · React Router
A machine-learning prototype for health-insurance premium prediction, combining exploratory modeling with a Flask-based prediction interface.
Stack: Python · scikit-learn · NumPy · Pandas · Flask
Python · PyTorch · TensorFlow · Keras · scikit-learn · NumPy · Pandas · OpenCV · Transformers
Django · Flask · Next.js · Payload CMS · REST APIs · MongoDB · PostgreSQL · MySQL
React · TypeScript · JavaScript · HTML · CSS · Tailwind CSS · Vite
Git · GitHub · Docker · Linux · AWS
- Google Scholar — publications and scholarly record
- ResearchGate — research profile and full-text research
- ORCID — persistent researcher identifier
- Personal website — projects, experience, and contact
The language card describes repository composition; it is not a measure of proficiency.
I welcome conversations about collaborative research, reproducible implementations, and applied AI systems in Biomedical AI, deep learning, medical image analysis, ECG and biomedical signals, computer vision, and applied machine learning.
Start a conversation on LinkedIn · Visit my website · Explore my repositories
