Website: https://asianwinterschool.github.io/index.html

Registration

Please click here to register. The deadline is Oct. 11th, 2024.

Introduction

The Asian Winter School (AWS) on Strings, Particles and Cosmology is a pan-Asian collaborative effort of high energy theorists from China, India, Japan and Korea to give young researchers in Asia an opportunity to come together and learn about the latest developments in high energy theory from leading experts on the subject.

This school is aimed towards advanced graduate students, postdoctoral fellows and active researchers in the field. This is the 19th in a series of Asian Winter Schools that have been organized on a rotating basis among China, Japan, India and Korea. We welcome students from all of these participating countries as well as students from outside.

The previous Asian Winter Schools in this series have provided young researchers with opportunities for discussions with leading experts in different areas and also for initiating collaboration with other young researchers belonging to the different participating countries. We hope the 2025 School will continue this tradition.

Invited Speakers

Jonathan J. Heckman (University of Pennsylvania):

Jonathan Sorce (Massachusetts Institute of Technology): 

gate io app (King's College London): 

Micha Berkooz (Weizmann Institute of Science): 

Victor A. Rodriguez (Princeton University): 

gate io (Massachusetts Institute of Technology):

Miguel Montero (Institute of Theoretical Physics in Madrid): 

Sabrina Pasterski (Perimeter Institute):

Vladimir Kazakov (École normale supérieure): 

Local Organization Committee

Bin Chen (Peking University)

Ling-Yan Hung (Tsinghua University)

Jianxin Lu (University of Science and Technology of China)

Wei Song (Tsinghua University)

Yinan Wang (Peking University)

Zhenbin Yang (Tsinghua University)

Steering Committee

Agnese Bissi (ICTP, Italy)

Bin Chen (Peking, China)

Atish Dabholkar (ICTP, Italy)

Rajesh Gopakumar (ICTS, India)

Koji Hashimoto (Kyoto, Japan)

Seok Kim (SNU, Korea)

Kimyeong Lee (KIAS, Korea)

Miao Li (ITP, CAS & Sun Yat-Sen, China)

Jian-Xin Lu (USTC, China)

Jun Nishimura (KEK, Japan)

Hirosi Ooguri (Caltech, USA & Kavli IPMU, Japan)

Ashoke Sen (ICTS, India)

Sang-Jin Sin (Hanyang, Korea)

Wei Song (Yau MSC, Tsinghua, China)

Tadashi Takayanagi (Yukawa ITP, Japan)

Spenta R. Wadia (ICTS, India)

Piljin Yi (KIAS, Korea)

Advisory Board

David Gross (Kavli Institute for Theoretical Physics)

Andrew Strominger (Harvard University)

Hirotaka Sugawara (OIST)

Shing-Tung Yau (Harvard University)

Course Organizers

Nabamita Banerjee (Indian Institute of Science Education and Research)

Seung-Joo Lee (IBS Center for Theoretical Physics of the Universe)

Honda Masazumi (RIKEN • Advanced Science Institute )

Onkar Parrikar (Tata institute of fundamental research)

Yinan Wang (Peking University)

Junggi Yoon (Asia Pacific Center for Theoretical Physics)

Masahito Yamazaki (Kavli Institute for the Physics and Mathematics of the Universe)

Zhenbin Yang (Tsinghua University)

Past Asian Winter Schools

KAWS 2024, Kyoto, Japan

KAWS 2023, Daejeon, Korea

KAWS 2022, online, India

KAWS 2021, online, China

KAWS 2020, Sendai, Japan

KAWS 2019, Seoul, Korea

KAWS 2018, Bangalore, India

AWS 2017, Zhuhai, China

AWS 2016, Okinawa, Japan

AWS 2015, Busan, Korea

AWS 2013, Puri, India

AWS 2013, Beijing, China

AWS 2012, Gunma, Japan

AWS 2011, Jeju, Korea

AWS 2010, Mahabaleshwar, India

AWS 2009, Beijing, China

AWS 2008, Gunma, Japan

AWS 2007, Pheonix Park, Korea

Supporting Organizations

会议摘要（Abstract）

本次研讨会以动力系统的最新进展及其在各个领域的应用为主题。动力系统研究系统随时间的演变，是当前数学研究的一个充满活力的领域。现代动力系统理论的起源于19世纪末期庞加莱在天体力学领域的开创性工作，之后由李雅普诺夫、伯克霍夫和斯梅尔等数学家进一步推进。该领域与数论、微分几何学、概率论等其他数学分支紧密相连，并在生物学、化学、物理学、工程学、气候科学、社会科学、工业数学、数据科学等多个学科领域展现出广泛而令人瞩目的应用前景。

本次研讨会包含动力系统和遍历理论方面的专家，他们将展示各自最新的研究成果，并就当前的前沿问题进行深入探讨。报告内容将包括微分动力系统、随机动力系统、光滑遍历论以及在其它相关领域的应用。

The topic of this workshop is recent advances in dynamical systems and their applications across various fields. Dynamical systems, which study the evolution of systems that change over time, is a vibrant area of current mathematical research. The modern theory of dynamical systems traces its origins to Poincaré’s work on celestial mechanics in the late 19th century, further developed by mathematicians including Lyapunov, Birkhoff and Smale. This field is closely connected to other areas like number theory, differential geometry and probability theory, and boasts a multitude of exciting applications in biology, chemistry, physics, engineering, climate science, social science, industrial mathematics, data science and more.

This workshop is to gather experts in dynamical systems and ergodic theory to share their latest research findings and discuss cutting-edge problems. Talks will include differentiable and stochastic dynamical systems, smooth ergodic theory and other related fields with applications in applied mathematics.

举办意义(Description of the aim)

本次研讨会将聚集动力系统领域活跃的资深专家与年轻学者。研讨会旨在分享与探讨最新的研究成果，推动正在进行中的项目及产生新的合作，并为探索跨学科方法提供一个交流平台。另外，研讨会将为年轻学者提供展示其最新研究成果的机会。

The proposed workshop aims to bring together active senior and young researchers in the field of dynamical systems. This gathering provides a platform for sharing and discussing the latest research findings, fostering new and ongoing collaborations, and exploring interdisciplinary approaches. In addition, the conference will provide opportunities for young scholars to present them recent work.

会议摘要（Abstract）

        近年来，随着数据类型与形式的多样化，对于复杂数据的研究吸引了越来越多学者的讨论与关注。网络数据作为一种常见的用于刻画不同个体之间属性与关系的复杂数据类型，逐渐引起了统计学，计算机科学，经济学，社会学等学科领域内学者的广泛关注。同时由于网络数据结构的复杂性，传统的数据分析方法可能无法解决网络数据分析中的问题，因此在这类数据的分析的过程中往往会产生新的统计分析方法。

现实生活中网络数据的一个重要特征是具有特定的群组结构，例如社交网络中不同的朋友圈，生态网络中不同的生物种群等等。因此，社区发现始终是网络分析中的一个重要问题。好的社区发现方法能够快速准确地找到网络数据中潜在的群组结构，从而更好地提取和利用网络数据中的信息。

        另一方面，随着数据收集能力的提升，网络数据的类型也更加多样化，更复杂的网络数据也通常包含更多的信息。例如对于动态网络数据的研究能够分析网络中结构与信息随时间的变化情况，对于超图网络的建模能够更好地理解网络中三个及以上个体之间的相互关系与作用。

        本次研讨会将重点关注网络数据中的社区发现，动态网络与超图网络的建模以及网络数据分析中的其他重要问题，旨在讨论网络数据分析领域最新的研究成果，促进学者之间的交流与合作。

The theme of this conference revolves around the evolving and multifaceted field of network data analysis. In recent times, the diversity of data types and formats has made the study of complex data an increasingly prominent topic within scholarly discussions and research. Network data, a complex data type commonly used to depict the attributes and relationships among different entities, has garnered extensive attention across multiple disciplines including statistics, computer science, economics, and sociology. This attention is partly due to the limitations of traditional data analysis methods in addressing the complexities inherent in network data, leading to the development of innovative statistical approaches for this purpose.

A notable feature of network data in real-world scenarios is its inherent group structures, such as the various social circles found in social networks or the different species populations in ecological networks. This aspect makes community detection a critical area in network analysis. Effective methods for community detection can identify potential group structures within network data quickly and accurately, thus improving the extraction and utilization of information from the network.

Additionally, advancements in data collection capabilities have led to a greater diversity in the types of network data available, which often contain more complex information. For example, studying dynamic network data can provide insights into how network structures and information change over time. Similarly, modeling hypergraph networks can enhance our understanding of the interactions and relationships among three or more entities in a network.

The focus of this conference will be on key areas such as community detection in network data, modeling of dynamic and hypergraph networks, and other important issues in network data analysis. The goal is to share and discuss the latest research findings in this field and to promote collaboration and exchange among scholars.

举办意义(Description of the aim)

        此次会议将涵盖网络统计分析的主要领域，包括社区检测，网络数据回归分析，动态网络建模，图模型概率性质等诸多领域。

        1.社区检测（community detection）

        社区检测是一种在网络数据中寻找紧密连接的子群体的方法。以社交网络为例，人们受工作环境、家庭和朋友的影响，自然地倾向于形成不同的群体。通过将网络划分为不同的社区，有助于揭示网络结构中的隐藏模式和关联。社区检测在社交网络分析、生物信息学和推荐系统等领域具有着广泛的应用，是网络统计分析中最热门的领域之一。

        经典的社区检测方法包括基于图分割、基于相似性度量、基于分裂和基于模块化优化等方法。近年来新的发展包括允许部分重叠的社区，动态的社区等。基于随机区块模型（Stochastic Block Model），许多学者对社区检测方法对准确度和渐近性质做了研究，例如Abbe (2017)， Wang et al.(2023)，Jin et al.(2023)等。此外，深度学习在社区检测中也具有潜力，例如Li et al.(2021)，Sperlí(2019)等。本次会议将邀请体现社区检测方面的现代发展，讨论前沿理论和应用问题。

        2.网络数据回归分析

        这一领域利用回归分析来理解和预测网络中节点的连接和交互方式，这有助于研究节点属性对网络拓扑的影响程度，并可以对网络中影响力较大的节点做出识别。相关模型包括稀疏Beta模型（Chen et al., 2020），全局结构模型（Ullah et al., 2021），根节点识别模型（Crane et al.,2021）等。

        3.动态网络建模

        网络时间序列正变得愈发常见，这些数据中相互关联的个体会影响彼此的特征。Zhu et al.(2017)提出了网络自回归模型，将时间与节点各自的协变量联系在一起。近年来，这一领域引起了广泛的关注，相关模型包括分位数自回归模型（Zhu et al., 2019），混合自回归模型（Zhu and Pan, 2020），网络霍克斯过程（Hawkes process）模型（Fang et al.,2023），自回归网络（Jiang et al.,2023）等。此外，考虑到同质性的存在，许多学者将潜在变量也考虑在内，如McFowland and Shalizi (2023)，Wu and Leng (2023)等。

        变点检测是一个与网络数据密切相关的问题，在动态网络框架下，近年来有许多相关的方法和理论结果，如Wang et al.(2021), Yu et al.(2021), Li et al.(2022)等。此次会议将请到相关邻域的学者进行讨论报告，分享前沿思想和技术。

        4.图模型概率性质

        当网络节点数趋于无穷时，网络的局部收敛性是非常重要的问题。以优先附着（preferential attachment）网络为例，节点数趋于无穷时它会局部收敛到一个特定的树过程（Garavaglia et al., 2022）。相关的理论结果包括Banerjee et al.(2023)，Hofstad(2024)等。

        此外，网络的概率性质与统计推断也密切相关，如网络数据的两样本检验等，相关工作包括Maugis et al.(2020)，Shao et al.(2022)等。此次会议将涵盖这一领域，邀请相关的概率统计学家进行报告，参会者将获得图模型的最新理论进展，使他们能够受到启发并推动网络统计分析的发展。

        5.超图网络

        常见的网络数据通常用于刻画不同节点两两之间的相互关系，超图网络则通过超边同时连接两个及以上的节点，从而能够表示多个节点之间的相互关系。这样的包含多个节点的相互关系在生活中也是十分常见的，例如生物医学中不同蛋白质之间复杂的相互关系。如何对超图网络进行合理的刻画和表述以及如何建立合理的超图网络模型是本次研讨会关注的问题。

This conference will delve into pivotal areas of network statistical analysis, focusing on community detection, regression analysis of network data, modeling of dynamic networks, understanding probabilistic properties in graph models, and exploring the complexities of hypergraph networks.

1. Community Detection.

This method identifies closely-knit subgroups within network data. For example, social networks often feature distinct groups formed around work, family, and friendships. By segmenting networks into various communities, we can uncover underlying patterns and connections. Community detection is crucial in fields like social network analysis, bioinformatics, and recommendation systems. Traditional methods include graph-based partitioning and modularity-based optimizations, while recent advances explore overlapping and dynamic communities. Notable research includes studies on the Stochastic Block Model's accuracy and properties. The conference will discuss these advancements, highlighting new theories and practical applications.

2. Network Data Regression Analysis.

This area uses regression analysis to understand and predict interactions within a network, analyzing how node attributes influence network structure and identifying key nodes. Models such as the Sparse Beta Model and Global Structure Model are notable examples. The conference will examine these models, contributing to our understanding of network relationships.

3. Dynamic Network Modeling.

As network time-series data gain prevalence, understanding how interconnected entities influence each other over time becomes crucial. Models like the network autoregressive model and Quantile Autoregressive Models have been developed to address this. The conference will also explore change-point detection within dynamic networks, a topic of growing interest, showcasing the latest methods and theories.

4. Probabilistic Properties of Graph Models.

A key focus here is the local convergence of networks as node numbers increase, exemplified by Preferential Attachment networks converging to specific tree processes. Theoretical contributions in this field include those by Banerjee et al. and Hofstad, providing insights into statistical inference in network data. The conference aims to delve into these probabilistic aspects, inviting experts to share their findings.

5. Hypergraph Networks.

Beyond conventional networks that link pairs of nodes, hypergraph networks connect multiple nodes simultaneously, enabling the representation of complex multi-node relationships. These are particularly relevant in fields like biomedicine for modeling intricate protein interactions. The seminar will focus on characterizing and modeling hypergraph networks, fostering a deeper understanding of these complex structures.

会议摘要（Abstract）

        我们计划在2024年12月于清华三亚国际数学论坛（TSIMF）举办第六届生物信息学和生物物理学中的算法和数学TSIMF国际会议。

        第六届生物信息学和生物物理学中的算法和数学TSIMF国际会议将汇集来自数学、生物统计学、化学、生物化学、生物物理学和分子/细胞生物学的研究人员，共同探讨如何将这些多样化的学科联系起来，并推动数学在解决生物物理学和生物信息学前沿问题中的应用。

        现代生命科学的一个重要趋势是，随着现代生物技术的出现，传统学科（如生理学、植物生物学、神经科学等）正在从宏观现象学过渡到基于分子结构的微观生物科学。与此发展并行，21世纪生命科学的一个主要特征是其从现象学和描述性学科向定量和预测性学科的转变。数学驱动的生物研究革命性机会已经出现。近年来，关于在分子和细胞尺度上的自组织生物系统的实验探索，包括SARS-CoV-2、分子马达和阿尔茨海默病中的蛋白质等，成为科学发现和创新的主导力量。然而，自组织生物系统的过度复杂性带来了定量描述的根本挑战，因为涉及过程的维数过高且过程复杂。能够有效减少自由度数量并建模复杂生物系统的数学方法在生物科学中变得越来越受欢迎。多尺度建模、流形提取、序列分析、拓扑简化、降维和机器学习技术被引入，以减少生物系统的复杂性，同时保持对感兴趣的分子和细胞的基本和充分描述。

        本次研讨会的主题将涵盖生物物理学和生物信息学中的各种数学模型及其在具体研究问题中的广泛应用。示例主题包括人类基因组的比较分析和分子进化、细菌耐药机制、基因组变异多态性、重要疾病的基因组关联、单细胞测序分析、高通量基因测序分析、基于微分几何的多尺度模型、生物分子的拓扑简化、GLMY同源性、自然向量、Yau-Hausdorff距离、几何代数、DNA、RNA和蛋白质的结理论、隐溶剂模型、泊松-玻尔兹曼方程、广义Born模型、极化连续体模型、积分方程模型、密度泛函方法、泊松-能斯特-普朗克方程、微观-宏观模型、连续-离散模型、微流控、生物分子运输、多尺度布朗动力学、电动学、电流体动力学、量子力学、分子力学和粗粒度模型。重点将放在上述模型、理论和方法在精准医学、病毒和微生物灾害的早期预警、基因组学、蛋白质组学、时空单细胞转录组数据处理的大数据数据库构建和检索技术、制药软件技术、DNA包装、DNA-蛋白质相互作用、蛋白质-蛋白质相互作用、蛋白质-配体相互作用、生物科学的数学AI、生物科学的拓扑深度学习、单细胞RNA序列分析、空间转录组数据、多组学分析、离子通道动力学、纳米孔膜中的离子运输、合理药物设计、药物发现和递送、大分子自组装和分子马达的动力学方面的应用。

举办意义(Description of the aim)

会议目标

        开发能够有效利用当前计算能力的多尺度数学模型。
        提升我们对人类基因组和生物分子结构、功能、动态和运输复杂性的理解。
        促进数学家、生物信息学科学家、生物物理学家和分子生物学家之间的新联系、互动和合作。
        提供一个交流生物信息学和生物物理学研究方法和成果的论坛。
        促进“生物学向数学”的信息流动，即向生物学研究生和近期获得博士学位的毕业生介绍新的生物启发数学模型。
        向研究生、博士后和初级教师介绍该领域和主题，帮助培训下一代该领域的研究人员。
        增强女性、代表性不足的少数族裔和残疾人士在分子基础数学生物学研究中的参与度。

会议影响

        目前，数学科学家在生物物理学和生物信息学领域工作的主要障碍是缺乏分子和细胞生物学知识，而生物学家的主要障碍则是疏于对过去几十年中发展起来的现代数学工具和技术的了解。这个研讨会系列旨在帮助弥合生物学家和数学科学家之间的差距，促进他们的合作。在这一领域，理论家和实验人员联合开发解决复杂问题的方案，具有巨大的整合跨学科研究潜力。这个研讨会系列将充当催化剂，充分利用这些协同作用，并创建一个合作网络，在本次研讨会结束后继续支持该领域的未来学术活动。

以往成就

        清华三亚国际数学论坛（TSIMF）年度“生物信息学和生物物理学中的算法和数学”研讨会系列自2018年至2023年已成功举办五届。前两届研讨会和最近的一届（2023年）在三亚线下举行，而第三届和第四届研讨会分别于2020年12月和2021年12月通过Zoom在线举行。

        为了记录这些CMBB研讨会，2018年至2022年间在期刊《信息与系统通讯》（Communications in Information & Systems, CIS）上发表了四期特刊：https://www.intlpress.com/site/pub/pages/journals/items/cis/_home/_main/index.php。这四期特刊中分别发表了6篇、10篇、7篇和5篇论文。第五届研讨会的特刊将于2024年在CIS上发表。

        这些以往的CMBB研讨会及相关特刊吸引了更多数学和生物学领域的人士参与生物物理学和生物信息学的建模与计算，并促进了他们之间的进一步合作。基于我们以往的成功成就，我们有信心提议的TSIMF研讨会系列将激发数学和计算的新研究方向，以应对生物物理学和生物信息学中的各种挑战。

参与人数

        在下方的初步拟邀参与者名单中，我们列出的人数超过了50人。但是并非每位受邀者都会接受我们的邀请。此外，我们将采用先到先得的原则，以确保最终参会人数不会超过50人。

Symposium Abstract

The purpose of this symposium is to bring together experts from various fields of singularity theory. These specialists' research areas will span from classical singularity theory to new branches of the exact and natural sciences, including mathematical modeling and its applications. We plan to invite keynote speakers who will showcase different aspects, primarily focusing on algebraic and analytic problems in singularity theory based on geometry. The discussions will center around singularity theory and related fields, including: singularities of smooth mappings and differential forms, subanalytic sets and semi-algebraic sets, Lipschitz stratifications, real algebraic singularities, Lagrangian and Legendrian singularities, asymptotic behavior of caustics and wavefronts, symplectic singularities, local invariants, symplectic singularities, contact and Poisson spaces, local algebras of singularities, resolutions of singularities, bifurcation of caustics and wavefronts, singular reduction, Hamiltonian systems and their generalizations, differential geometry of singularities, affine invariants of curves and surfaces, free divisors, torus actions, topology of singularities, and  singularities of positive characteristic  and singularities in algebraic geometry theory.

Description of the Aim

This symposium also aims to highlight the significance of mathematics in industrial and nanoscale sciences, particularly in nanomedicine. Mathematical and computational methods play a pivotal role in the theoretical understanding of nanomaterials. Both approaches provide effective theoretical and simulation tools for analyzing and interpreting experimental results, predicting the quantitative and qualitative behavior based on models, and controlling nanoscale systems. Mathematics also plays a crucial role in the interaction of these diverse disciplines, as they all rely on data, simulation, and visualization.

Speakers

Yifan Chen (Tsinghua University) -On stringy invariant of $\mathbb{Q}$-Gorenstein varieties via $\mathbb{Q}$-embedded resolution

Yifei Chen (Institute of Mathematics, Chinese Academy of Sciences) -Flops connecting minimal models

Jinsan Cheng (Chinese Academy of Science) -Singularities of the intersection of parametric surfaces and its applications

Huhe Han (Northwest A&F University) -TBA

Hiroyuki Hayashi (Kobe university) -Geometry on curves passing through Whitney umbrella

Chuangqiang Hu (BIMSA) -On the k-th Tjurina number of weighted homogeneous singularities

Goo Ishikawa (Hokkaido University) -Japan Notes on singularities theory of mappings over dual numbers

Miyuki Koiso (Kyushu University) -Intrinsic singular points and curvatures of piecewise-smooth surfaces and their applications

Junzhen Li (Kobe University, Japan) -Geometry of Gluing Developable Surfaces

Yanlin Li (Hangzhou Normal University) -The singularities and geometrics of evolutoids of non-lightlike surfaces in Minkowski 3-space

Guorui Ma (Tsinghua University) -Low-Dimensional Tori in Calogero-Moser Systems

Fanning Meng (Guangzhou University) -TBA

Graham Reeve (Liverpool Hope University) -TBA

Agustin Romano-Velazquez (University of Mexico) -Reflexive modules on quotient surface singularities

Kentaro Saji (Kobe University, Japan) -Normal form of the central singularities of D4-bifurcation of fronts and its applications

Quan Shi (Tsinghua University) -Motivic Principal Value Integral for Hyperplane Arrangements

Ishii Shihoko (The University of Tokyo) -Lifting of ideals in positive characteristic to those in characteristic zero

Siyong Tao (Tsinghua University) -Bernstein-Sato roots for weighted homogeneous polynomials in positive characteristic

Asahi Tsuchida (Shiga University, Japan) -TBA

Xun Yu (Tianjin university) -TBA

Beihui Yuan (BIMSA) -TBA

Chunping Zhong (Xiamen University) -Schwarz lemma for Finsler metrics on the classical domains

Call for Presentations

We kindly invite all speakers to submit their presentation abstracts for the symposium. Please click the link below to access the submission page and follow the provided instructions to complete the required information. We look forward to your valuable contributions!

https://conf.issa.bimsa.cn/formrender/formview/FT1I7NGVB5IQCVVF?appkey=A1I7NGTQBTQMVVE&lang=en-US