光華講壇——社會名流與企業家論壇第6613期

主 題：Individualized Dynamic Model for Multi-resolutional Data with Application to Mobile Health應用于移動健康的多分辨率數據個性化動態模型

主講人：加州大學爾灣分校 Annie Qu教授

主持人：統計學院 林華珍教授

時間：7月16日 15:00-16:00

舉辦地點：柳林校區弘遠樓408會議室

主辦單位：統計研究中心和統計學院 國際交流與合作處 科研處

主講人簡介：

Annie Qu is Chancellor’s Professor, Department of Statistics, University of California, Irvine. She received her Ph.D. in Statistics from the Pennsylvania State University in 1998. Qu’s research focuses on solving fundamental issues regarding structured and unstructured large-scale data and developing cutting-edge statistical methods and theory in machine learning and algorithms for personalized medicine, text mining, recommender systems, medical imaging data, and network data analyses for complex heterogeneous data. The newly developed methods can extract essential and relevant information from large volumes of intensively collected data, such as mobile health data. Her research impacts many fields, including biomedical studies, genomic research, public health research, social and political sciences. Before joining UC Irvine, Dr. Qu was a Data Science Founder Professor of Statistics and the Director of the Illinois Statistics Office at the University of Illinois at Urbana-Champaign. She was awarded the Brad and Karen Smith Professorial Scholar by the College of LAS at UIUC and was a recipient of the NSF Career award from 2004 to 2009. She is a Fellow of the Institute of Mathematical Statistics (IMS), the American Statistical Association, and the American Association for the Advancement of Science. She is also a recipient of IMS Medallion Award and Lecturer in 2024. She serves as Journal of the American Statistical Association Theory and Methods Co-Editor from 2023 to 2025 and as IMS Program Secretary from 2021 to 2027.

Annie Qu，加州大學爾灣分校統計系Chancellor’s Professor。她于1998年獲得賓夕法尼亞州立大學統計學博士學位。她的研究重點在于解決結構化和非結構化大規模數據的基本問題，并開發尖端的統計方法和理論，應用于機器學習和個性化醫學算法、文本挖掘、推薦系統、醫學影像數據以及復雜異質數據的網絡數據分析。新開發的方法可以從大量密集收集的數據（例如移動健康數據）中提取重要且相關的信息。她的研究影響了多個領域，包括生物醫學研究、基因組研究、公共衛生研究、社會和政治科學。

在加入加州大學爾灣分校之前，她是伊利諾伊大學厄巴納-香檳分校的統計學Data Science Founder Professor，并擔任伊利諾伊大學厄巴納-香檳分校統計學辦公室主任。她被 UIUC 的 LAS 學院授予 Brad and Karen Smith Professorial Scholar，并在 2004-2009 年獲得 NSF Career award。她是國際數理統計學會（IMS）、美國統計學會（ASA）和美國科學促進會（AAAS）的Fellow,她還是IMS Medallion Award and Lecturer 獲得者。她是JASA Theory and Methods的co-editor（2023-2025），并從2021年到2027年擔任IMS Program Secretary。

內容簡介：

Mobile health has emerged as a major success in tracking individual health status, due to the popularity and power of smartphones and wearable devices. This has also brought great challenges in handling heterogeneous, multi-resolution data which arise ubiquitously in mobile health due to irregular multivariate measurements collected from individuals. In this paper, we propose an individualized dynamic latent factor model for irregular multi-resolution time series data to interpolate unsampled measurements of time series with low resolution. One major advantage of the proposed method is the capability to integrate multiple irregular time series and multiple subjects by mapping the multi-resolution data to the latent space. In addition, the proposed individualized dynamic latent factor model is applicable to capturing heterogeneous longitudinal information through individualized dynamic latent factors. In theory, we provide the integrated interpolation error bound of the proposed estimator and derive the convergence rate with B-spline approximation methods. Both the simulation studies and the application to smartwatch data demonstrate the superior performance of the proposed method compared to existing methods.

移動健康由于智能手機和可穿戴設備的普及和強大功能，已經成為追蹤個人健康狀態的重大成功。這也帶來了處理異質、多分辨率數據的巨大挑戰，因為這些數據由于個體不規則的多變量測量而普遍存在于移動健康中。在本文中，主講人提出一種用于不規則多分辨率時間序列數據的個性化動態潛在因子模型，以插值低分辨率時間序列的未采樣測量值。該方法的一個主要優勢是能夠通過將多分辨率數據映射到潛在空間來整合多個不規則時間序列和多個個體。此外，所提出的個性化動態潛在因子模型適用于通過個性化動態潛在因子捕捉異質縱向信息。在理論上，主講人提供所提估計器的集成插值誤差界限，并通過B樣條近似方法推導出收斂速率。模擬研究和智能手表數據的應用都表明，所提方法相較于現有方法具有優越的性能。