On December 5, the School of Statistics and Data Science of Southeast University held a cooperation exchange meeting with the Xuanwu District Science and Technology Bureau at Jiangsu International Data Port.The meeting focused on joint laboratory development and deeper industry–university–research collaboration. Through a combination of discussion and site visits, the parties advanced their cooperation from preliminary planning to on-site alignment. The meeting laid a solid foundation for future collaboration in joint talent development and the sharing of computing resources.
At the meeting, representatives of the School first introduced its existing computing resources. The School has already purchased and deployed two large GPU computing servers. It can also draw on the facilities of the University’s Big Data Center for flexible supplementary computing support. These resources will provide hardware support for the proposed joint laboratory.
Drawing on its site conditions and technical strengths, the Data Port proposed a phased approach to cooperation. A professional team will coordinate the planning of server room layout, computing resource scheduling, and the cybersecurity framework. The two sides plan to enable compliant and secure resource access through technologies such as VPNs and bastion hosts. They also reached preliminary consensus on joint course development, the establishment of a practice base, and monthly coordination meetings. Both sides agreed to further review the University’s incubation policies and the enterprise resources available for collaboration, so as to accelerate the implementation of specific cooperation plans.
After the meeting, representatives of the School toured the open office area, dedicated university–enterprise collaboration workstations, and multifunctional seminar rooms. The visit gave them a deeper understanding of the Data Port’s operational model and service support capacity. They also inspected the core server room, modular computing facilities, and the full-process security system, gaining direct insight into a high-standard, secure, and well-managed computing operation system.
In the large-model training area and the inference application demonstration area, the representatives closely observed the full workflow of large-model development, from data annotation and algorithm training to scenario-based applications. They also held on-site discussions on integrating large-model technologies into course teaching and jointly developing application-oriented research projects. These exchanges further expanded the scope for future industry–university–research collaboration.
This event, organized in the form of discussion plus site visits, not only clarified the practical path for cooperation, but also transformed the idea of collaboration from a written plan into a visible and tangible platform. Going forward, the School and the Data Port will accelerate the establishment of a student practice and innovation base, the launch of courses supported by external mentors, and the initiation of the first batch of practice-oriented projects. In doing so, they will further advance their shared goal of resource sharing, complementary strengths, and collaborative talent cultivation.
