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11-28
2025
量子学百年：从微观结构到宏观量子效应 ——解读2025年诺贝尔物理学奖的科学意义
演讲人：孙昌璞 [中国工程物理研究院研究生院]时间：16:00-17:30, Nov 28, 2025 (Fri)地点：Lecture Hall, FIT Building内容：量子力学诞生百年之际，2025 年诺贝尔物理学奖授予宏观系统量子隧道效应及能级量子化的开创性实验工作。围绕此次获奖果的科学意义，报告人首先从科学发展的逻辑角度回顾量子力学发展的百年历程：第一阶段是“第一次量子化”（1925年-1930年代中期）：对单粒子系统的正则量子化，建立波函数与算符体系...
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11-28
2025
Local Computation of PageRank: Simple and Optimal
演讲人：Hanzhi Wang [University of Melbourne]时间：14:00-15:00, Nov 28, 2025 (Fri)地点： RM 1-222, FIT Building内容：PageRank is a classic node centrality measure in graphs, originally proposed by the co-founders of Google to rank web pages in their search engine. With the expansion of the web and modern networks, an important problem is the design of local algorithms that estimate a node’s Pa...
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11-27
2025
从能量的视角观察看Transformer
演讲人：刘勇（中国人民大学）时间：11:45-13:00, Nov 27, 2025 (Thu)地点：RM 1-202, FIT Building内容：Transformer 是当前大模型的核心架构，在自然语言处理、计算机视觉等领域展现出卓越性能，但其内在机理的理解仍较为有限。本报告从统计物理能量视角构建新框架，发现自注意力机制本质是最小化 Helmholtz 自由能的过程，由此为其提供了物理层面的原理解释。在此基础上，进一步从能量视角设计了新型 Transformer 架构，且实...
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11-24
2025
Fast and Good Correlation Clustering
演讲人：Mikkel Thorup [University of Copenhage]时间：14:00-15:00, Nov 24, 2025 (Mon)地点：RM 1-222, FIT Building内容：A clustering is a partitioning into sets called clusters. The input to (unweighted) correlation clustering is an unweighted graph. We want to cluster the vertices so as to minimimize the number of edges between clusters plus the number of non-edges inside clusters.Recently there...
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11-07
2025
Beyond ASICs: GPU-Accelerated FHE and Evolving LLM Strategies on Programmable Hardware
演讲人：Jung Ho Ahn [首尔国立大学] 时间：10:00-11:00, Nov 7, 2025 (Fri)地点：RM 1-222, FIT Building内容：GPUs continue to redefine performance boundaries across diverse workloads. This talk presents two compelling examples from our latest research, underscoring the critical role of programmable accelerators. First, we demonstrate how our group surpassed DARPA's ambitious 2021 DPRIVE ASIC target...
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11-06
2025
LLM theory for training wireless networks
演讲人：Lingjie Duan [SUTD]时间：14:00-15:00, Nov 6, 2025 (Thu)地点：FIT1-312内容：Existing AI methods (model-based optimization, deep reinforcement Learning) struggle with generalization and rapid adaptation in dynamic wireless networks. This work pioneers "LLM for Wireless," harnessing the general intelligence of Large Language Models (LLMs) to proactively optimize network infrastructure, mov...