Hey, I’m Dr. Wang
Building AI-native systems that connect materials research, intelligent maintenance, and real-world engineering.
Now
2026 · Nanjing
Organizing specialist knowledge, AI collaborators, and engineering validation into one continuously improving workflow.
Advancing field validation of road materials research and intelligent maintenance. Open to discussions on specific maintenance challenges, trial settings, and joint validation.
Building multimodal materials databases and knowledge graphs to make research evidence traceable and reusable. Open to exchanging ideas on organizing materials data, connecting knowledge, and research methods.
Exploring how AI collaborators can support research, product development, and entrepreneurship. Open to exchanging ideas on practical workflows, building tools together, and collaboration experiences.
Nanjing Daily · Xinhua Daily · Yangtse Evening Post / Purple Cow News · Nanjing Broadcasting Corporation · Jiangsu Economic News
The press archive lists 6 reports from 5 outlets. Publication dates range from to . The linked press page provides each report’s summary and source links.
The 2 honors below are documented in official award notices. Each entry identifies the award and Dr. Wang’s role in the team or as a faculty supervisor.
From road materials to intelligent maintenance and OPC collaboration, selected paths from research to engineering.
An industry AI platform connecting pavement inspection, decisions, materials, experiments, construction, and feedback.
It turns multimodal field and laboratory data into traceable, scenario-aware maintenance decisions.
Intelligent maintenance · Platform R&DReleased; partner trials (reported 2026-04-30)Project details & evidence
A domain knowledge and decision engine for pavement materials and maintenance.
It connects literature, standards, engineering cases, and materials data to generate explainable, auditable recommendations.
Domain AI · Knowledge reasoningProject description; release status unverifiedProject details & evidence
A multimodal materials database and knowledge graph for data-driven research.
It links composition, processing, test conditions, microstructure, performance, and conclusions so evidence can be reused and traced.
Materials informatics · Knowledge graphProject description; release status unverifiedProject details & evidence
Automated, high-throughput experiments for faster and more reproducible materials validation.
The system records parameters, conditions, and results throughout the workflow, shortening the path from formulation to engineering evidence.
Laboratory automation · Experimental validationProject description; release status unverifiedProject details & evidence
Open to collaborations in road materials, intelligent maintenance, AI research platforms, and OPC initiatives.
