丁劍，國際概率論領域的杰出學者，國家級人才稱號，現(xiàn)任北京大學數(shù)學科學學院講席教授、博士生導師，并兼任大數(shù)據(jù)分析與應用技術國家工程實驗室聯(lián)聘教授。他于2006年獲北京大學數(shù)學學士學位，2011年獲加州大學伯克利分校博士學位，曾在斯坦福大學、華盛頓大學及數(shù)學科學研究所（MSRI）從事博士后研究，歷任芝加哥大學統(tǒng)計系助理教授、終身副教授，賓夕法尼亞大學統(tǒng)計系終身副教授及Gilbert Helman講席教授，2022 年全職加入北大。丁劍教授的研究聚焦概率論與統(tǒng)計物理、理論計算機科學的交叉領域，在隨機約束滿足問題、隨機平面幾何、安德森局域化、無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論與復雜系統(tǒng)科學的內(nèi)在聯(lián)系。他在 Acta Math.、Ann. Math.、Invent. Math.等頂級期刊發(fā)表論文50余篇，提出隨機約束相變分析框架，建立隨機平面幾何漸近理論，解決無序系統(tǒng)臨界行為關鍵問題，并在隨機環(huán)境中的隨機游走、隨機薛定諤算子等領域做出系統(tǒng)性貢獻。作為國際學術領軍者，丁劍教授受邀在2022年國際數(shù)學家大會及2024年國際數(shù)學物理大會作特邀報告，長期擔任 J. Amer. Math. Soc.、Ann.Probab.等期刊編委，屢獲英國 Rollo Davidson 獎（2017）、世界華人數(shù)學家大會數(shù)學金獎（2022）、科學探索獎（2023）及法國 Loève 概率獎（2023）等殊榮。他致力于數(shù)學教育，主講《隨機過程 II》課程，培養(yǎng)學生創(chuàng)新思維與解決復雜問題的能力，以跨學科視野推動概率論與人工智能、量子物理等領域的深度融合，成為全球數(shù)學界極具影響力的學者。
報告摘要：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 Erd?s–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.
