kaiyun体育官方网站统计学首届校友学术论坛第二期将于北京时间2022年7月30日上午举行，主题是：“从网络到时间序列：驭数有道的统计学”（Statistics Harnessing the Power of Data）。本期将邀请罗格斯大学陈嵘教授、卡内基梅隆大学金加顺教授、密歇根大学朱冀教授三位校友作报告分享，之后进行圆桌讨论。根据疫情防控要求，本期论坛将继续采用Zoom线上会议形式进行。
我们诚挚邀请北大校友、海内外统计师生及统计学爱好者前来参会！
第二期论坛时间：北京时间2022年7月30日，08:30-11:40
Zoom会议ID：886 5202 4737 (密码: 220730）
会议链接：https://us06web.zoom.us/j/88652024737?pwd="aXpLSUVFTXdYVlgvVFliTUFXdmxadz09
报告人介绍：
Rong Chen，Department of Statistics，Rutgers University
Dr. Chen is Distinguished Professor of Statistics, and Chair of Department of Statistics at Rutgers University. His teaching and research interests include analysis of complex time series and dynamic systems, Monte Carlo methods and statistical applications in bioinformatics, business and economics, and engineering. He is an elected Fellow of the American Statistical Association and the Institute of Mathematical Statistics. Dr. Chen served as a co-editor of Journal of Business and Economic Statistics and is currently serving as a co-editor of Statistica Sinica. He is former Treasurer of the Institute of Mathematical Statistics and former program director in the Division of Mathematical Sciences of National Science Foundation. Dr. Chen received both his Ph.D. and M.S. in Statistics from Carnegie Mellon University and his B.S. in Mathematics from the Peking University.
Title: Statistical Learning of Modern Time Series
In the BIGDATA era, many new forms of data have become available and useful in various important applications. When these data are observed over time, they form new types of time series that require new statistical models and analytical tools in order to extract useful information and to make prediction. In this talk we present new developments in analyzing matrix/tensor time series, dynamic transport networks, functional time series and compositional time series, with applications ranging from economics, finance, international trade, and many others. We will also briefly discuss approaches on modeling other forms of time series, including text time series and dynamic social networks.
Jiashun Jin，Department of Statistics，Carnegie Mellon University
Jiashun Jin earned his Ph.D in statistics from Stanford University in 2003, and is currently Professor of Statistics & Data Science and affiliated Professor in Machine Learning at Carnegie Mellon University. One of his main research interests is in Large-Scale Inference, especially in the regime where the signals of interest are both rare and weak. He is best known for his work on Tukey’s Higher Criticism and his work on phase transition for Rare/Weak signals. His more recent research interest is analysis of social networks. Jin has received several awards and honors. He has delivered the highly selective IMS Medallion Lecture in 2015 and the IMS Annals of Applied Statistics (AOAS) Lecture in 2016. He has also received the NSF CAREER Award in 2007, IMS Tweedie Award in 2009. He is an elected IMS fellow and an elected ASA fellow. He has severed as an Associate Editor for multiple journals and is currently IMS treasurer.
Title: The Statistical Triangle
We have collected and cleaned a data set consisting of the citation and bibtex (e.g., title, abstract, author information) data of 83, 331 papers published in 36 journals in statistics and related fields, spanning 41 years. Using the data set, we constructed 21 co-citation networks, each for a time window between 1990 and 2015. We propose a dynamic DegreeCorrected Mixed-Membership (dynamic-DCMM) model, where we model the research interests of an author by a low-dimensional weight vector (called the network memberships) that evolves slowly over time. We propose dynamic-SCORE as a new approach to estimating the memberships. We discover a triangle in the spectral domain which we call the Statistical Triangle. The triangle is reminiscent of the philosophical triangle of statistics by Efron (1998), but our triangle is based on analysis of real data. We use the Statistical Triangle to visualize the research trajectories of individual authors. We interpret the three vertices of the triangle as the three primary research areas in statistics: “Bayesian”, “Biostatistics” and “non-parametrics”. The Statistical Triangle further splits into 15 subregions, which we interpret as the 15 representative sub-areas in statistics. These results provide useful insights over the research trend and behavior of statisticians. We also discuss the optimality of Mixed-SCORE.
Ji Zhu，Department of Statistics，University of Michigan
Ji Zhu is Susan A. Murphy Professor of Statistics at the University of Michigan, Ann Arbor. He received his B.Sc. in Physics from Peking University, China in 1996 and M.Sc. and Ph.D. in Statistics from Stanford University in 2000 and 2003, respectively. His primary research interests include statistical machine learning, high-dimensional data modeling, statistical network analysis, and their applications to health sciences. He received an NSF CAREER Award in 2008, and was elected as a Fellow of the American Statistical Association in 2013 and a Fellow of the Institute of Mathematical Statistics in 2015. He has also been rated as an ISI Highly Cited Researcher from 2014-2020 by Web of Science, which publishes an annual list recognizing leading researchers in the sciences and social sciences from around the world.
Title: Population-level Balance in Signed Networks
Statistical network models are useful for understanding the underlying formation mechanism and characteristics of complex networks. However, statistical models for signed networks have been largely unexplored. In signed networks, there exist both positive (e.g., like, trust) and negative (e.g., dislike, distrust) edges, which are commonly seen in real-world scenarios. The positive and negative edges in signed networks lead to unique structural patterns, which pose challenges for statistical modeling. In this talk, we introduce a statistically principled latent space approach for modeling signed networks and accommodating the well-known balance theory, i.e., "the enemy of my enemy is my friend" and "the friend of my friend is my friend". The proposed approach treats both edges and their signs as random variables, and characterizes the balance theory with a novel and natural notion of population-level balance. This approach guides us towards building a class of balanced inner product models, and towards developing scalable algorithms via projected gradient descent to estimate the latent variables. We also establish non-asymptotic error rates for the estimates, which are further verified through simulation studies. We apply the proposed approach to an international relation network, which provides an informative and interpretable model-based visualization of countries during World War II.
