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南方科技大学-新加坡国立大学联合研究项目招聘博士后1名
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The project is hiring 1 postdoctoral fellows at SUSTech for Image Processing and Deep Learning). 南方科技大学课题组现公开招聘博士后1名(图像处理和深度学习方向). Title/项目名称 Tensor Dimension Reduction for Multimodal Data and its Application in EEG Analysis（多模态张量降维方法研究和在EEG数据分析中的应用） Abstract/项目简介 Multimodal data analysis, driven by the increasing variety of data types in applications, has attracted much interest in statistics, focusing on encoding different modalities into a common representation space to build predictive models or explore relationships between modalities. The main goal of the project is to build models to describe complicated dependencies between modalities. This is particularly challenging when at least one modality is a tensor due to the complexity of tensor structures. Our idea is to introduce a novel Structural Equation Model (SEM) based tensor matrix factorisation model to simultaneously extract latent scores and build relationships between modalities. The context of the proposal is the study of insomnia patients, where EEG data and questionnaire responses are analysed to understand the relationship between these very different types of data. The project outlines four specific problems, including the development of new tensor matrix factorisation models, the investigation of identifiability conditions, the measurement of non-linear dependence in tensor decomposition, and the study of functional connectivity models in the context of tensor matrix factorisation. statistical inference, implementation tools, optimisation algorithms and theory of both statistical and optimisation errors will be studied. Applications to the analysis of other wearable and mobile data will also be explored. The project will include but is not limited to the following four topics (1) SEM-based tensor matrix factorization model; (2) Nonlinear tensor matrix factorisation models and identifiability; (3) Tensor Decomposition with a measure of nonlinear dependence; (4) Functional connectivity analysis based on factorisation model. 由于在实际应用中的数据类型日益多样化，多模态数据分析引起了统计学界的极大兴趣。其核心在于将不同模态的数据编码到一个共同的空间中，以建立预测模型或探索各模态之间的关系。该项目的主要目标是建立模型来描述模态之间潜在的依赖关系。由于涉及张量结构的复杂性，当至少一种模态为张量时，这项工作尤其具有挑战性。我们的思路是引入一种新颖的基于结构方程模型的张量矩阵因式分解模型，以同时提取潜在变量并建立模态之间的关系。该项目的背景是针对失眠症患者的研究，通过分析脑电图数据和问卷答复来了解这些截然不同数据类型之间的关系。该项目包含了四个具体问题，包括开发新的张量矩阵因式分解模型、研究可识别性条件、测量张量分解中的非线性依赖性以及在张量矩阵因式分解背景下研究功能连接模型。该项目研究范围涵盖了统计推断、实现工具、优化算法以及统计和优化误差理论。此外，还将探讨该方法在其他可穿戴和移动数据分析方面的应用潜力。该项目将包括但不限于以下四个主题：(1)基于结构方程模型的张量矩阵因式分解模型；(2)非线性张量矩阵因式分解模型与可识别性；(3)考虑非线性依赖的张量分解；(4)基于因式分解模型的功能连接分析。 |
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PIs at SUSTech Dr. Chao Wang （王超博士） Dr. Wang is an Assistant Professor of the Department of Statistics and Data Science at Southern University of Science and Technology. His research directions are mainly image processing, scientific computing, and interdisciplinary data science. He has published over 30 papers in top-tier journals such as the SIAM series and IEEE Transactions, as well as leading conferences, and has received notable accolades including the Best Paper Award at the 2022 CVPR Workshop, the Shenzhen Pengcheng Peacock Plan Distinguished Professorship (2021), and the Best Paper Award at the 2017 Annual Meeting of China Society for Industrial and Applied Mathematics. He has led research projects such as the National Natural Science Foundation of China Youth Program, Guangdong Basic and Applied Research Foundation Program, and Shenzhen Science and Technology Program, while also contributing as a principal investigator or core member to major initiatives including the National Key Research and Development Program, Hong Kong RGC Research Fund projects, and Shenzhen Fundamental Research Program.王超，南方科技大学统计与数据科学系副研究员，博导，其研究方向主要为图像处理、科学计算与交叉学科的数据科学。在本领域期刊SIAM系列、IEEE汇刊等杂志及学术会议发表学术论文三十余篇。在2022年CVPR研讨会获得最佳论文，在2021年获深圳市鹏城孔雀计划特聘岗位，在2017年获得中国工业与应用数学学会年会最佳论文。主持国自然青年基金、广东省面上基金以及深圳市稳定支持面上项目，以课题负责人或核心成员参与国家重点研发项目、香港研资局科研基金项目以及深圳重点项目。 王超副研究员个人网页https://wangcmath.github.io/ |
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