Shenzhen MSU-BIT University Successfully Hosts the Conference-School on Tensor Methods in Mathematics and Artificial Intelligence Computing (2025)

From November 10 to November 19, the Conference-School on Tensor Methods in Mathematics and Artificial Intelligence Computing (2025) was successfully held at Shenzhen MSU-BIT University (SMBU). The event was jointly organized by SMBU, the Institute of Computational Mathematics of the Russian Academy of Sciences, and Moscow State University, co-sponsored by the Mathematical and Intelligence Branch of Operations Research Society of China, with support from the Moscow Computational Acceleration Laboratory, Moscow Radio Transmission Technology Laboratory, and CHASPARK Technology Website. The conference attracted academicians and experts from overseas universities in Russia, Italy, France, and brought together scholars from domestic universities including Fudan University and Harbin Institute of Technology (Shenzhen), creating a shared platform for international academic exchange.

At the opening ceremony, SMBU President Li Hezhang warmly welcomed the visiting scholars and experts. In his speech, he emphasized that SMBU, guided by the goal of “rooted in both countries, serving the world, leading innovation,” has been deeply engaged in fundamental research and frontier exploration, committed to building an international and interdisciplinary academic network. He noted that, in today’s world at the intersection of technological revolution and industrial transformation, strengthening international academic collaboration is an inevitable choice to drive scientific progress.

SMBU First Vice President Sergey Ivanchenco highlighted in his remarks that SMBU, as a key platform for Sino-Russian joint education, has since its founding carried the important mission of building bridges for educational cooperation and promoting scientific and cultural exchange between the two countries. He expressed the hope that this conference would further establish a high-level platform for academic exchange and collaboration, advance the study of tensor methods in artificial intelligence and data science, and help researchers from both countries build consensus, deepen cooperation, and jointly promote development.

During the conference, 30 high-quality academic presentations were delivered, covering topics such as new algorithms and theories for tensor decomposition, advanced optimization techniques, randomized methods and noise data processing, applications of low-rank tensors and their optimization in data science, and applications of tensor and optimization methods in wireless communications.

Within the mathematical framework of artificial intelligence and machine learning, tensor methods have become a core language for representation and computation. Extending from vectors and matrices in linear algebra, tensor methods generalize the mathematical language of AI to arbitrary dimensions, forming a computational cornerstone for the deep learning era. As a natural extension of scalars, vectors, and matrices, tensors unify heterogeneous data representations—such as images, text, and graph structures—into multidimensional arrays, enabling neural networks to handle the complexity of the real world within a single framework. More importantly, tensor operations are not only a core unit for modern GPU parallel acceleration but also provide deep insights for model compression, latent variable analysis, and algorithm design through theoretical tools such as decomposition and approximation. Today, tensor methods have evolved beyond mere representation tools to become a crucial bridge linking mathematical abstraction with engineering practice, reshaping modeling and solution paradigms in artificial intelligence problems.

The conference provided experts and scholars in AI and tensor research with a high-level platform for exchange and collaboration. Through active discussion and interaction, participants further advanced communication and mutual learning in this research field. The successful organization of the event not only laid a solid foundation for future cooperation in AI and tensor research but also provided strong momentum for deepening Sino-Russian collaboration in education, research, and innovation. SMBU will continue to leverage its strengths in Sino-Russian joint education, deepen collaboration with leading global universities, research institutes, and industry partners, and promote the establishment of regular exchange, joint research, and talent cultivation mechanisms. Using this conference as a starting point, the university aims to build academic consensus, accelerate the translation of tensor method research, ensure frontier technologies better serve high-quality global economic and social development, and contribute to the building of a shared future for humanity.