Research
Four lines of work, each held to the evidence it needs.
Four research areas: AI and education, multi-agent intelligence and collaboration, AI and biological sciences, and applied AI in engineering.
01
2 subareas
Artificial Intelligence & Education
We explore the intersection of AI and education through two complementary perspectives: using AI to support learning tools and educational assessment, and investigating how generative AI affects evaluation, text authenticity, and educational practices. Our empirical and technical work emphasizes reliability, scalability, and practical value.
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02
2 subareas
Multi-Agent Intelligence & Collaboration
This area examines AI-agent collaboration, tool use, and complex task execution, exploring how structured workflows, development tools, and application systems improve practical agent capabilities. Publicly available tools are organized by their research and application context rather than placed in a separate open-source category.
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03
2 subareas
AI & Biological Sciences
Our work explores the two-way relationship between AI and the biological sciences, including machine learning applications in genomics, neuroscience, and biomedical data, as well as computational ideas inspired by biological systems. We emphasize scientific questions, appropriate computational methods, and evidence-based evaluation.
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04
2 subareas
Applied AI & Engineering
We explore applications of AI to engineering science, materials design, and practical technical challenges. In sustainable concrete research, we integrate experimental data, machine learning, and environmental assessment to investigate the relationships among material composition, mechanical performance, and embodied carbon, and to support data-driven decisions under engineering constraints.
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