商品描述
作者簡介
Alejandra Avalos Pacheco is a tenure-track Universitätsassistentin at the Institute of Applied Statistics at JKU Linz, Austria, and an affiliated member of the Harvard-MIT Center for Regulatory Science at Harvard University. She earned her PhD in Statistics through the joint CDT program between the University of Warwick and the University of Oxford. Her thesis received the prestigious Savage Award in Applied Methodology. She has held postdoctoral positions at Harvard University and worked at the Dana-Farber Cancer Institute. Additionally, she served as a research fellow at the University of Florence and a non-tenure-track Universitätsassistentin at TU Wien. Her research focuses on creating interpretable, computationally efficient models for large, complex data, particularly in medical applications such as cancer. She specializes in Bayesian and probabilistic machine learning, with expertise in high-dimensional inference, dimensionality reduction, graphical models, data integration and clinical trials. Fan Bu is a tenure-track Assistant Professor in Biostatistics at the University of Michigan. She completed her Ph.D. in Statistics at Duke University and was previously a postdoctoral research fellow at UCLA, where she developed Bayesian methods for large-scale observational health data. Her research spans Bayesian modeling for temporal and spatio-temporal processes, networks, and federated data, with applications in health data science and observational studies for comparative effectiveness and safety and has appeared in leading journals such as the Journal of the American Statistical Association and Statistics in Medicine. An active member of the Observational Health Data Sciences and Informatics (OHDSI) collaborative, Bu contributes to statistical methods development and leads large-scale network studies to improve health decisions and patient care. Beatrice Franzolini is a Researcher at the Bocconi Institute for Data Science and Analytics, Bocconi University, Milan, Italy. She is a statistician specializing in Bayesian statistical theory, methods and application, with a particular focus on Bayesian nonparametrics. Her research encompasses random probability measures, species sampling models, dependent random partitions, and dynamic models. She has published in leading journals such as Biometrika and The Annals of Applied Statistics. Franzolini holds a Ph.D. from Bocconi University and has held research positions at the Agency for Science, Technology, and Research in Singapore, as well as the Division of Biomedical Data Science at the National University of Singapore's medical school. Beniamino Hadj-Amar is a Postdoctoral Fellow in the Department of Statistics at Rice University, Houston, TX. His research focuses on Bayesian methods for analyzing complex dynamical time series, with expertise in latent structure identification, non-stationary and non-linear processes, and sparse data structures. He holds a Ph.D. from the Oxford-Warwick Statistics Programme (OxWaSP). Hadj-Amar's methodological toolkit includes switching models, change-point detection, Bayesian nonparametrics, graphical models, and statistical spectral analysis. His work is applied to neuromodulation, respiratory research, and circadian studies, leveraging diverse datasets such as electrophysiological signals, wearable device data, and fMRI. His contributions have appeared in prestigious journals such as the Journal of the American Statistical Association and The Annals of Applied Statistics.
作者簡介(中文翻譯)
Alejandra Avalos Pacheco 是奧地利林茲約翰·凱爾大學應用統計研究所的終身教職助理教授，並且是哈佛大學哈佛-麻省理工學院監管科學中心的附屬成員。她通過華威大學和牛津大學的聯合 CDT 計劃獲得統計學博士學位。她的論文獲得了應用方法學的著名 Savage 獎。她曾在哈佛大學擔任博士後職位，並在達納-法伯癌症研究所工作。此外，她還曾在佛羅倫斯大學擔任研究員，以及在維也納科技大學擔任非終身教職的助理教授。她的研究專注於為大型複雜數據創建可解釋且計算效率高的模型，特別是在癌症等醫療應用中。她專精於貝葉斯和概率機器學習，擁有高維推斷、降維、圖形模型、數據整合和臨床試驗的專業知識。
Fan Bu 是密西根大學生物統計學的終身教職助理教授。她在杜克大學完成統計學博士學位，並曾在加州大學洛杉磯分校擔任博士後研究員，開發大規模觀察性健康數據的貝葉斯方法。她的研究涵蓋了時間和時空過程的貝葉斯建模、網絡和聯邦數據，應用於健康數據科學以及比較效果和安全性的觀察性研究，並發表在《美國統計協會期刊》和《醫學統計學年鑑》等領先期刊上。作為觀察性健康數據科學與信息學（OHDSI）合作組織的活躍成員，Bu 為統計方法的開發做出貢獻，並領導大規模網絡研究以改善健康決策和病人護理。
Beatrice Franzolini 是意大利米蘭博科尼大學數據科學與分析研究所的研究員。她是一名專注於貝葉斯統計理論、方法及應用的統計學家，特別關注貝葉斯非參數方法。她的研究涵蓋隨機概率測度、物種抽樣模型、依賴隨機劃分和動態模型。她曾在《生物統計學》和《應用統計學年鑑》等領先期刊上發表過文章。Franzolini 擁有博科尼大學的博士學位，並曾在新加坡科學技術研究局及新加坡國立大學醫學院的生物醫學數據科學部門擔任研究職位。
Beniamino Hadj-Amar 是德克薩斯州休斯頓萊斯大學統計學系的博士後研究員。他的研究專注於分析複雜動態時間序列的貝葉斯方法，擁有潛在結構識別、非平穩和非線性過程以及稀疏數據結構的專業知識。他擁有牛津-華威統計計劃（OxWaSP）的博士學位。Hadj-Amar 的方法工具包包括切換模型、變更點檢測、貝葉斯非參數方法、圖形模型和統計頻譜分析。他的工作應用於神經調節、呼吸研究和晝夜節律研究，利用多樣的數據集，如電生理信號、可穿戴設備數據和功能性磁共振成像（fMRI）。他的貢獻已發表在《美國統計協會期刊》和《應用統計學年鑑》等著名期刊上。
