About me
Hello! I’m Mingda Zhang, an undergraduate at the School of Software, Yunnan University (2022.9–2026.6). I work on vision–language models, multimodality, NLP, and computer vision, aiming to tightly integrate language and visual signals for real‑world applications in medical imaging and legal analysis.
I’m fortunate to be mentored by Professor Jianglong Qin (Software, YNU), Professor Qing Xu (Law, YNU), and Professor Xiaoyang Tan (NUAA), whose guidance has helped me develop a systematic, problem‑driven approach to AI research.
Education and Internships
- Yunnan University, School of Software — B.Eng. (2022.9 – 2026.6)
- China Telecom, Chongqing Branch — Technical Engineer (2024.7 – 2024.8)
- The Chinese University of Hong Kong, Shenzhen — Visiting Student (2025.10 – 2026.4)
Research Highlights
Medical Image Analysis & Segmentation
- Author/co‑author on lung disease recognition in collaboration with Army Medical University (AMU).
- Dataset: Privately processed dataset with institutional ethics approval (IRB/ethics cleared; contains sensitive medical data and cannot be released publicly).
➤ Access: available to qualified researchers under a data‑use agreement and ethics compliance. (Contact Me: [email protected])
Applications of Large Language Models
- Collaborated with the President of an Intermediate People’s Court; our system received multiple letters of recommendation.
- Sep 2025: National Social Science Fund of China (NSSFC) project officially approved —
“An Empirical Study on the Mechanism of Judicial Justice in the Digital‑Intelligence Era under Socialism with Chinese Characteristics.” - Number:25CFX009
GitHub — 160★ Project
Efficient fine‑tuning of LLMs for the medical vertical.
Others
- ICASSP 2026 reviewer
- University-level scholarship
- National Gold Award, “New Humanities and Social Sciences Practice and Innovation Competition for College Students”
Papers
“Unified Multimodal Coherent Field: Synchronous Semantic–Spatial–Vision Fusion for Brain Tumor Segmentation” (ICASSP 2026, CCF-B) — Proposed a UMCF method that synchronously fuses visual, semantic and spatial information in a 3D latent space. (Mingda Zhang, Yuyang Zheng, Ruixiang Tang, Jingru Qiu, Haiyan Ding) PDF
“DCFFSNet: Deep Connectivity Feature Fusion Separation Network for Medical Image Segmentation” (ICASSP 2026, CCF-B) — Proposed a feature space decoupling strategy that addresses the segmentation fragmentation problem caused by forced feature coupling. (Mingda Zhang, Xun Ye, Ruixiang Tang, Jianglong Qin) PDF
“Multimodal Fusion at Three Tiers: Physics-Driven Data Generation and Vision-Language Guidance for Brain Tumor Segmentation” (Array, JCR Q1) — Bidirectional interaction between VLM models and deep learning for brain tumor recognition. (Mingda Zhang, Kaiwen Pan) PDF
“The Consistency-Acceptability Divergence of LLMs in Judicial Decision-Making: Task and Stakeholder Dimensions” (Humanities & Social Sciences Communications, JCR Q1 / CAS Q1) — Proposing the concept of Consistency-Acceptability Divergence to reveal social phenomena in current LLMs. (Mingda Zhang, Xu Qing) PDF
“Knowledge-Guided Brain Tumor Segmentation via Synchronized Visual-Semantic-Topological Prior Fusion” (BMC Medical Imaging, JCR Q1) — Integrating VLM models and deep learning for brain tumor recognition. (Mingda Zhang — solo author) PDF
“An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis” (Scientific Reports, JCR Q1) — Combining prompt engineering with knowledge graphs for legal analysis. (Mingda Zhang, Na Zhao, Jianglong Qin, Qing Xu, Kaiwen Pan, Ting Luo) PDF
“A Multi-granularity Sparse Concept Activation and Hierarchical Knowledge Graph Fusion Framework for Rare Disease Diagnosis” (iScience, JCR Q1) — Addressing knowledge deficiency and algorithms to LLM in rare disease diagnosis. (Mingda Zhang, Na Zhao, Jianglong Qin, Guoyu Ye, Ruixiang Tang) PDF
“A Method for the Architecture of a Medical Vertical Large Language Model Based on Deepseek R1” (CCF-AI-2025) — Designing a lightweight LLM architecture for the bioinformatics field. (Mingda Zhang, Jianglong Qin) PDF
“ELPG-DTFS: Prior-Guided Adaptive Time-Frequency Graph Neural Network for EEG Depression Diagnosis” (ICASSP 2026, CCF-B) — Modeling EEG dynamic connectivity through channel-band attention, learnable connections and residual prior integration. (Jingru Qiu, Jiale Liang, Xuanhan Fan, Mingda Zhang, Zhenli He) PDF
“A Semantic Segmentation Algorithm for Pleural Effusion Based on DBIF-AUNet” (Computer Technology and Development) — Semantic segmentation of pleural effusion CT is achieved through dual-domain feature disentanglement and multi-branch interactive attention. (Ruixiang Tang, Mingda Zhang, Jianglong Qin, Yan Song, Yi Wu, Wei Wu) PDF