报告人：中国海洋大学 刘彬教授
报告时间：2025年11月15日10:50
报告地点：科技园阳光楼南815
报告题目：An Overview of Fair Submodular Maximization：Models and Approximation Algorithms
摘要：
Submodular functions capture the property of diminishing returns and play a central role in both combinatorial optimization and machine learning. Many problems—such as minimum spanning tree, maximum matching, max cut, exemplar-based clustering, recommender systems, data subset selection, and social network analysis—can be formulated as the submodular optimization problems under various constraints and then solved efficiently using approximate algorithms.
However, in real-world datasets, elements often possess diverse attributes, including but not limited to age, gender, or race. This diversity has brought increasing attention to the problem of fair submodular maximization, which aims to balance utility and fairness across different groups. In this talk, I will present several results on fairness constrained submodular maximization and discuss recent developments.
报告人简介：
刘彬，中国海洋大学数学科学学院教授、博导、院长助理。2010年毕业于山东大学运筹学与控制论专业，获理学博士学位。研究领域和兴趣包括：组合优化、近似算法的设计与分析、图论及其应用等。在Journal of Global Optimization、Journal of Graph Theory、IEEE Transactions on Network Science and Engineering、SCIENCE CHINA Mathematics、Journal of the Operations Research Society of China等期刊和INFOCOM等会议发表论文60余篇，先后主持省部级以上科研项目共6项（其中国家自然科学基金面上项目2项）。先后担任中国工业与应用数学学会副秘书长、图论组合及应用专委会常务委员、信息和通讯技术领域的数学专委会委员，中国运筹学会理事、图论组合分会理事和副秘书长、数学规划分会副秘书长和青年支部副主任，山东省运筹学会理事和青年工作委员会主任等。
