National Key R&D Program of China
Creativity evaluation for key technologies in intelligent interactive experimental teaching.
Hello, I am
Ph.D. Candidate · Multimodal & Embodied Intelligence
Beijing University of Posts and Telecommunications
Zhongguancun Laboratory
I am a jointly trained Ph.D. candidate working on multimodal large language models, embodied geo-localization, and world models. I am interested in how intelligent agents can actively seek visual evidence, reason about space, and make reliable decisions in open environments.
我是北京邮电大学与中关村国家实验室联合培养博士生,研究方向包括多模态大模型、具身地理定位与世界模型。
Background
Jointly trained with Zhongguancun Laboratory. My research focuses on multimodal large language models, embodied geo-localization, and world models.
Worked on cell-nucleus segmentation and intelligent analysis for digital pathology images.
Conducted undergraduate research in digital holography and acoustic levitation.
Research in practice
Creativity evaluation for key technologies in intelligent interactive experimental teaching.
Multimodal perception and autonomous agent workflows for space-target understanding.
Led research and delivery of a meta-learning system for network-state diagnosis.
Led a student research project on deep-learning-based grading of breast-cancer biopsy slides.
Updates
ERGeoBench was accepted by ICML 2026.
CreBench was published at AAAI 2026.
Selected as an Outstanding Graduate Student, top 5% of the program.
Received the First Prize of the Science and Technology Progress Award from the China Association of Work Safety.
Research outputs
An evidential learning approach that quantifies predictive uncertainty to detect unseen log anomalies in open settings.
A latent evidence-search framework for informative viewpoint selection and pose-aware spatial belief.
Recognition