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. An important task before matching is to test whether two graphs are indeed correlated (ie, the detection problem). In this talk, I will present some recent progress on detecting correlations between two graphs sampled from stochastic block models, featuring the algorithmic and computational issues. Our work is inspired by recent work on detecting correlations for Erdos-Renyi graphs.  This is based on joint work with Guanyi Chen, Shuyang Gong and Zhangsong Li.

丁剑教授于2006 年获北京大学学士学位，2011 年获美国加州大学伯克利分校博士学位。他曾先后在斯坦福大学从事博士后研究工作，历任芝加哥大学、宾夕法尼亚大学教职人员，现任北京大学讲席教授。

丁剑教授的研究方向为概率论，重点关注概率论与统计物理、理论计算机科学、统计学习理论的交叉领域，同时也致力于探究源于“应用导向型” 问题的概率论课题。他与多位合作者共同开展研究，成果涵盖随机约束满足问题、随机平面几何、随机场伊辛模型、随机环境中的随机游走以及随机薛定谔算子等多个方向。

丁剑教授获得多项学术荣誉，包括2015 年斯隆研究奖、2017 年罗洛・戴维逊奖、2022 年国际华人数学家大会金奖、2022年国际数学家大会邀请报告、2023年科学探索奖、2023年概率论洛伊芙奖，以及2025年新基石研究员。