Speakers 2025

Keynote Speaker Ⅰ

 Prof. Bin Guo

Northwestern Polytechnical University, China

Bio: Dr. Bin Guo is currently a professor with Northwestern Polytechnical University (NPU), China. He was previous a post-doctoral researcher of Prof. Daqing Zhang's research group at Institute TELECOM SudParis, France. He received his Ph.D. degree in computer science from Keio University, Tokyo, Japan, in 2009. He completed his Ph.D studies in the Anzai-Imai Lab, under the supervision of Prof. Michita Imai and Prof. Yuichiro Anzai (president of JSPS in Japan).  He received his B.E and M.E of Computer Sciencefrom Xi'an Jiaotong University in 2003 and 2006, respectively. He serves as the vice-dean of the School of Computer Science, the deputy director of the Intelligent Sensing and Computing Lab of the Ministry of Industry and Information Technology of China, the founding director of the AI&Art Research Center of NPU，and the Secretary General of the Belt and Road AIoT International Cooperative Alliance (BRAVE).

Speech title: From Crowd Sensing to Crowd Computing—Harnessing the Power of the Crowd

Abstract: Mobile Crowd Sensing (MCS), as a new sensing paradigm that harnesses the power of the crowd, has become a promising research field in recent years. Numerous studies have been done on the research challenges such as optimized worker selection, incentive mechanisms, efficient data transmission, crowd data quality/trust, novel MCS applications, and so on. In this talk, we will discuss about the recent development and future directions of MCS. In particular, we will talk about Crowd Computing, a novel collaborative computing paradigm for heterogeneous Human-Machine-Things fusion, to build a crowd-intelligence computing space with the capabilities of self-organization, self-adaptation, and continuous evolution. We will report the recent progress of our group towards this promising research area.

Keynote Speaker Ⅱ

 Assoc. Prof. Luoyi Fu

Shanghai Jiao Tong University, China

Bio: Luoyi Fu, Associate Professor at the School of Computer Science, Shanghai Jiao Tong University, and recipient of the National Science Fund for Distinguished Young Scholars. Research focuses on knowledge mining and discovery inspired by graph networks. Luoyi Fu is the recipient of multiple awards, including the First Prize of the 2021 CCF Natural Science Award (Rank 1), the 2022 CCF Young Scientist Award, Top 10 2019 N²Women Rising Stars in Computer Networking and Communications， and the 2016 ACM China Doctoral Dissertation Award. She is the author of the textbook Introduction to Mobile Internet (3rd and 4th editions).

Speech title: De-anonymization Techniques Driven by Network Structural Characteristics

Abstract: With the rapid development of Internet technologies, users enjoy unprecedented convenience while facing escalating risks of cybersecurity breaches and privacy leakage. In academia, extensive research has been conducted to address these challenges. Among them, one of the most fundamental and classical directions is Network De-anonymization, whose core objective lies in quantitatively assessing the power of de-anonymization attacks or measuring the accuracy of de-anonymizing anonymized graphs. Currently, a large number of de-anonymization attack algorithms as well as anonymization protection mechanisms have been proposed. However, an important open question remains: How effective is a given de-anonymization algorithm when applied to anonymized graphs produced by different protection schemes? Answering this question not only quantifies the strength of a de-anonymization algorithm but also provides a way to evaluate the robustness of anonymization techniques against specific attacks.

In this presentation, I will share several representative works conducted by myself and my collaborators in this research area. Specifically, we establish a theoretical connection between graph symmetry and de-anonymizability, where we quantify the upper bound of de-anonymizability through symmetry measures. For general seedless-based de-anonymizability, we further express its upper bound as the sum of the diagonal elements of a doubly stochastic matrix representing vertex correspondences between the anonymized and auxiliary graphs. This analytical framework not only provides a theoretical upper bound for de-anonymization but also inspires the design of a motif-based sampling algorithm that efficiently extracts symmetric structures within large-scale graphs. Building upon this foundation, we have also carried out a series of derivative explorations that deepen our understanding of network structure–driven de-anonymization mechanisms. Overall, our research on network de-anonymization offers both theoretical insight and practical reference for real-world attack modeling, privacy protection, and quantitative assessment. The topological structure of graphs plays a critical role across all these aspects. Leveraging richer topological information for enhanced privacy research remains a key direction, offering abundant opportunities for deeper exploration in the future.

Keynote Speaker Ⅲ

Prof. Caigui Jiang

Xi'an Jiaotong University, China

Bio: Dr. Caigui Jiang, is a professor at Institute of Artificial Intelligence and Robotics of Xi’an Jiaotong University, honored with the National-level Young Talent award. He received his B.S. and M.S. degrees from Xi’an Jiaotong University (XJTU) in 2008 and 2011 respectively, and obtained his Ph.D. in Computer Science from King Abdullah University of Science and Technology (KAUST) in 2016. After that, he conducted postdoctoral research at the Max Planck Institute for Informatics in Germany (2016-2018), served as a Research Scientist at the International Computer Science Institute (ICSI), UC Berkeley (2018-2019), and continued his research as a Scientist at the Visual Computing Center of KAUST (2019-2021). Professor Jiang returned to Xi'an Jiaotong University at the end of 2021. His primary research focuses on computer graphics, 3D geometry processing, computer vision, and robotics. He has authored numerous publications in the field's most prestigious venues, including SIGGRAPH, SIGGRAPH Asia, and ACM Transactions on Graphics (TOG). He leads several national and ministerial research initiatives, securing grants from programs such as the National Key R&D Program of China and the National Natural Science Foundation of China.

Speech title: Geometric Modeling from Flat Sheet Material

Abstract: This presentation summarizes my research on computational methods for designing 3D structures from flat sheet materials. I will explore three core techniques rooted in this principle: Origami (folding), Kirigami (cutting and folding), and Developable Surfaces (smooth, unbendable shapes). The talk is structured around three key projects:（1）Curved Pleated Structures:I will present a novel method for designing origami with complex curved folds. These structures can approximate free-form surfaces, act as flexible mechanisms, and be flattened without gaps, with applications in architecture and robotics. （2）Free-form Quad-based Kirigami:This work extends traditional "pop-up" structures to create complex, curving 3D shapes from a single, flat sheet of material. I will introduce a new "discrete expanding mapping" technique to achieve this, demonstrated with physical models. （3）Shape-Morphing Mechanical Metamaterials: Here, I focus on "auxetic" materials that expand when stretched. I will explain a new "constant mean stretch" mapping method that allows us to design a single flat sheet that can morph between two specific 3D shapes, even enabling bistable structures with two stable states. Throughout the talk, I will emphasize the underlying geometric principles, our computational pipeline involving specialized initializations and constraint-based optimization, and the validation of our designs through physical prototypes. This research bridges geometric modeling, material science, and digital fabrication.