Yamir Moreno

Yamir Moreno 教授任职于西班牙萨拉戈萨大学理论物理系，现为生物计算与复杂系统物理研究所（BIFI）主任、复杂系统与网络实验室（COSNET）负责人。2000年在萨拉戈萨大学获物理学博士学位，长期从事复杂系统的理论基础研究，以物理学、数学和网络科学方法分析不同尺度下的结构与动力学问题。

其研究涉及信息与疾病传播、网络扩散、非线性动力学、复杂社会系统及多层网络等方向。曾任网络科学学会和复杂系统学会主席，获得复杂系统学会高级科学奖等荣誉。本次报告将讨论网络动力学中的群体、其信息表示所需的比特代价，以及群体结构对谣言传播的影响。

刘雪明 / Xueming Liu

刘雪明教授任职于华中科技大学人工智能与自动化学院，长期从事网络科学、计算生物信息学及人工智能在生物系统中的应用研究，曾获国家自然科学基金优秀青年科学基金项目资助。其研究重点包括多层生物分子网络的构建与鲁棒性分析、网络可控性，以及复杂网络分析方法在生物分子系统中的应用。

相关成果发表于 Nature Communications、PNAS、Physics Reports、Engineering、Physical Review E 等学术期刊，曾获湖北省自然科学一等奖与中国 ACM SIGBIO 分会学术新星奖。本次报告围绕有向网络的韧性分析及其生物学应用展开，关注连接的方向性、网络之间的依赖关系和系统失效过程对整体功能稳定性的影响。

秦伯韡 / Bo-Wei Qin

秦伯韡是复旦大学智能复杂体系基础理论与关键技术实验室副研究员、博士生导师，获国家自然科学基金优秀青年科学基金项目资助。2011年和2017年分别在香港城市大学获得数学学士和博士学位，此后在复旦大学数学科学学院从事博士后研究。其研究长期聚焦应用数学与复杂性科学，尤其关注非线性动力系统中的分岔理论及其高效分析方法。

其研究问题覆盖基因调控线路、神经网络、生态系统与社会系统等多种复杂场景，相关成果发表于 Physical Review Letters、Nature Communications、SIAM Journal on Applied Dynamical Systems、Physica D 等期刊。本次报告以复杂振荡调控为主题，从非线性动力学与复杂系统的角度讨论振荡行为的产生、变化与控制，为理解网络化系统中的集体动力学提供理论视角。

周斌 / Bin Zhou

周斌教授任职于南京信息工程大学管理工程学院，获中国科学技术大学理学博士学位。其研究以大数据统计分析为基础，主要涉及复杂系统、网络科学与计算社会科学，重点关注真实网络的结构特征、演化规律以及网络结构与社会行为之间的联系。

其团队在 Nature Communications 发表的 “The nature and nurture of network evolution” 研究，通过结合节点内在吸引力与既有连接结构，探讨不同类型网络的形成与演化机制。相关研究成果还发表于 Nature Human Behaviour、PNAS、Social Networks 等学术期刊。本次报告将围绕复杂网络建模的最新进展，讨论如何从观测数据提炼可解释的网络生成与演化机制，以及如何在理论模型与真实世界网络之间建立联系。

孙永征 / Yongzheng Sun

孙永征教授任职于中国矿业大学数学学院，博士生导师、学院科研副院长，兼任江苏省应用数学中心副主任等职务。2010年获复旦大学数学科学学院理学博士学位，曾赴牛津大学、剑桥大学牛顿数学研究所等机构进行访问交流。主要研究方向包括生物数学、复杂网络、多智能体系统、随机动力学、混沌以及人工智能与大数据的交叉应用。

其研究成果发表于 Physical Review Letters、SIAM Journal on Applied Mathematics、Chaos、Physical Review E、IEEE Transactions on Network Science and Engineering 等期刊。研究关注复杂系统的数学建模，以及噪声、耦合和非线性机制对集体行为的影响。本次报告以复杂网络系统中振荡的自发调节为主题，从动力学机制角度讨论网络化系统的自组织行为与调节过程。

刘权辉 / Quanhui Liu

刘权辉，四川大学计算机学院副教授、博士生导师，入选四川省学术和技术带头人后备人选、四川大学“双百人才工程”计划和四川大学青年科技学术带头人。主要研究方向为人工智能与预防医学交叉，关注复杂网络、数据驱动的传染病传播建模与智能预测。

在 PNAS、Nature Communications 等期刊及 AAAI、ACM MM 等会议发表学术论文40余篇，主持国家重点研发计划课题、国家社科重大项目课题、国家自然科学基金等科研项目10余项，担任中国人工智能学会社会计算与社会智能专业委员会、中国自动化学会计算社会与社会智能专业委员会委员。本次报告将梳理传染病建模与预测从传统数据驱动、深度学习和物理信息方法，发展到大模型、世界模型与智能体驱动方法的研究脉络。

Yamir Moreno

Professor Yamir Moreno is a Professor of Theoretical Physics at the University of Zaragoza, Director of the Institute for Biocomputation and Physics of Complex Systems (BIFI), and head of the Complex Systems and Networks Lab (COSNET). He received his PhD in Physics from the University of Zaragoza in 2000. His research investigates the foundations of complex systems by combining ideas and methods from physics, mathematics and network science.

His work spans information and disease spreading, diffusion processes, nonlinear dynamics, computational social science and multilayer networks. He previously served as President of the Network Science Society and of the Complex Systems Society, and has received the Complex Systems Society Senior Scientific Award. His invited talk explores groups in network dynamics, the information cost of representing them in bits, and their implications for rumor spreading.

Xueming Liu

Professor Xueming Liu is with the School of Artificial Intelligence and Automation at Huazhong University of Science and Technology. His research covers network science, computational bioinformatics and applications of artificial intelligence to biological systems. He has received support through the National Natural Science Foundation of China's Excellent Young Scientists Fund. His work includes constructing and analyzing multilayer biomolecular networks, investigating network robustness and controllability, and applying network-based approaches to problems in molecular biology.

His findings have appeared in journals including Nature Communications, PNAS, Physics Reports, Engineering and Physical Review E. His research has received the Hubei Provincial Natural Science Award (First Prize) and the ACM SIGBIO China Rising Star Award. His presentation examines the resilience of directed networks and their biological applications, highlighting how directionality, interdependence and structural organization shape system stability.

Bo-Wei Qin

Bowei Qin is an Associate Researcher and doctoral supervisor at the Laboratory for Intelligent Complex Systems at Fudan University. He is a recipient of the National Natural Science Foundation of China's Excellent Young Scientists Fund. He earned his bachelor's and doctoral degrees in mathematics from City University of Hong Kong in 2011 and 2017, respectively, and subsequently worked as a postdoctoral researcher at Fudan University's School of Mathematical Sciences.

His research lies at the intersection of applied mathematics and complexity science, with particular emphasis on bifurcation theory and efficient analytical methods for nonlinear dynamical systems. Applications of his work include gene regulatory circuits, neural networks, ecological systems and social systems. His publications have appeared in Physical Review Letters, Nature Communications, SIAM Journal on Applied Dynamical Systems and Physica D. His talk on modulating complex oscillations connects the theory of nonlinear dynamics with the understanding and control of collective behavior in complex systems.

Bin Zhou

Professor Bin Zhou is with the School of Management Science and Engineering at Nanjing University of Information Science and Technology. He holds a PhD in science from the University of Science and Technology of China. Drawing on large-scale statistical analysis, his research explores complex systems, network science and computational social science, especially the structure and evolution of real-world networks and their connections to social behavior.

His work on network evolution, published in Nature Communications as “The nature and nurture of network evolution,” investigates how intrinsic node attractiveness and accumulated connectivity jointly shape network growth. His publications also include studies in Nature Human Behaviour, PNAS and Social Networks. In this workshop, his talk on advances in complex network modeling offers a perspective on building interpretable models of network formation and evolution from empirical observations.

Yongzheng Sun

Professor Yongzheng Sun is a Professor and doctoral supervisor in the School of Mathematics at China University of Mining and Technology, where he also serves as Associate Dean for Research. He received his PhD in mathematics from Fudan University in 2010 and has undertaken academic visits to institutions including the University of Oxford and the Isaac Newton Institute at the University of Cambridge. His research interests span biomathematics, complex networks, multi-agent systems, stochastic dynamics, chaos, and interdisciplinary applications of artificial intelligence and data science.

His work has been published in journals including Physical Review Letters, SIAM Journal on Applied Mathematics, Chaos, Physical Review E and IEEE Transactions on Network Science and Engineering. His research seeks mathematical explanations for collective behavior and the roles of coupling, randomness and nonlinear interactions. His invited presentation addresses the spontaneous regulation of oscillations in complex network systems, connecting microscopic dynamical mechanisms with emergent system-level behavior.

Quanhui Liu

Quanhui Liu is an Associate Professor and doctoral supervisor at the School of Computer Science, Sichuan University. He has been selected for Sichuan Province’s reserve program for academic and technical leaders, Sichuan University’s Double Hundred Talents Program, and its Young Scientific and Academic Leaders Program. His research focuses on the intersection of artificial intelligence and preventive medicine, including complex networks, infectious disease modeling and intelligent prediction.

He has published more than 40 papers in journals including PNAS and Nature Communications and at conferences such as AAAI and ACM MM. He has led more than ten research projects and subprojects supported by national and provincial programs, and serves on professional committees in social computing and social intelligence. His presentation traces the evolution of infectious disease modeling and prediction from data-driven approaches and deep learning to physics-informed methods, large models, world models and agent-driven approaches.