丁剑，国际概率论领域的杰出学者，国家级人才称号，现任北京大学数学科学学院讲席教授、博士生导师，并兼任大数据分析与应用技术国家工程实验室联聘教授。他于2006年获北京大学数学学士学位，2011年获加州大学伯克利分校博士学位，曾在斯坦福大学、华盛顿大学及数学科学研究所（MSRI）从事博士后研究，历任芝加哥大学统计系助理教授、终身副教授，宾夕法尼亚大学统计系终身副教授及Gilbert Helman讲席教授，2022 年全职加入北大。丁剑教授的研究聚焦概率论与统计物理、理论计算机科学的交叉领域，在随机约束满足问题、随机平面几何、安德森局域化、无序自旋模型等方向取得突破性成果，其工作深刻揭示了概率论与复杂系统科学的内在联系。他在 Acta Math.、Ann. Math.、Invent. Math.等顶级期刊发表论文50余篇，提出随机约束相变分析框架，建立随机平面几何渐近理论，解决无序系统临界行为关键问题，并在随机环境中的随机游走、随机薛定谔算子等领域做出系统性贡献。作为国际学术领军者，丁剑教授受邀在2022年国际数学家大会及2024年国际数学物理大会作特邀报告，长期担任 J. Amer. Math. Soc.、Ann.Probab.等期刊编委，屡获英国 Rollo Davidson 奖（2017）、世界华人数学家大会数学金奖（2022）、科学探索奖（2023）及法国 Loève 概率奖（2023）等殊荣。他致力于数学教育，主讲《随机过程 II》课程，培养学生创新思维与解决复杂问题的能力，以跨学科视野推动概率论与人工智能、量子物理等领域的深度融合，成为全球数学界极具影响力的学者。
报告摘要：A basic goal for random graph matching is to recover the vertex correspondence between two correlated graphs from an observation of these two unlabeled graphs. Random graph matching is an important and active topic in combinatorial statistics: on the one hand, it arises from various applied fields such as social network analysis, computer vision, computational biology and natural language processing; on the other hand, there is also a deep and rich theory that is of interest to researchers in statistics, probability, combinatorics, optimization, algorithms and complexity theory. Recently, extensive efforts have been devoted to the study for matching two correlated Erdős–Rényi graphs, which is arguably the most classic model for graph matching. In this talk, we will review some recent progress on this front, with emphasis on the intriguing phenomenon on (the presumed) information-computation gap. In particular, we will discuss progress on efficient algorithms thanks to the collective efforts from the community. We will also point out some important future directions, including developing robust algorithms that rely on minimal assumptions on graph models and developing efficient algorithms for more realistic random graph models. This is based on joint works with Guanyi Chen, Yumou Fei, Hang Du, Shuyang Gong, Zhangsong Li and Yuanzheng Wang.
