This research program focuses on the design and implementation of collaborative, cross-institutional learning environments that connect students, researchers, and industry partners across universities and countries. It integrates artificial intelligence, data-driven problem solving, and interdisciplinary collaboration to address real-world societal challenges.

The program adopts Global Shared Learning Classroom (GSLC) and Collaborative Online International Learning (COIL) models, enabling students to work on joint projects using real datasets from domains such as food systems, climate change, public health, energy, and infrastructure. These collaborations bridge academic learning with global problem-solving, preparing students for multidisciplinary and international research environments.

A key objective of this program is to transform traditional teaching into collaborative research-driven learning, where students co-develop solutions using AI, optimization, and engineering systems approaches. The program also supports the development of globally competent graduates with strong analytical, technical, and collaborative skills.

Key Research Areas

• Global Shared Learning Classroom (GSLC) and COIL-based education models

• AI-driven collaborative problem solving using real-world datasets

• Interdisciplinary learning across engineering, health, environment, and social systems

• Data-driven approaches to global challenges (food, climate, health, infrastructure)

• Cross-institutional research collaboration and co-supervision

• Digital learning platforms and collaborative education technologies

• Development of globally competitive and research-ready graduates

Featured Outputs

• Collaborative student research projects using real-world datasets

• Joint interdisciplinary and cross-university research initiatives

• AI-driven solutions addressing global societal challenges