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Unless otherwise noted, all talks are in 120 Hanes Hall, at 3:30 PM on Mondays. Prior to the talk, from 3:00-3:30 PM, the audience is invited for refreshments in the lounge on the 3rd floor of Hanes Hall. If you would like to suggest a speaker, or get on our mailing list, please send an email to Dr. Gabor Pataki or Dr. Kai Zhang.

In addition to weekly colloquia and seminars, Hotelling lectures are held to honor the memory of Professor Harold Hotelling, first chairman of the “Department of Mathematical Statistics.”

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Past Events › STOR Colloquium

October 2019

STOR Colloquium: Heping Zhang, Yale

October 14, 2019 @ 3:30 pm - 4:30 pm

Back to the Basics: Residuals and Diagnostics for Generalized Linear Models

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STOR Colloquium: Shujie Ma, UC Riverside

October 21, 2019 @ 3:30 pm - 4:30 pm

Shujie Ma University of California, Riverside   How many communities are there in a network?   Advances in modern technology have facilitated the collection of network data which emerge in many fields including biology, bioinformatics, physics, economics, sociology and so forth. Network data often have natural communities which are groups of interacting objects (i.e., nodes); pairs of nodes in the same group tend to interact more than pairs belonging to different groups. Community detection then becomes a very important task,…

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November 2019

STOR Colloquium: Jianqing Fan, Princeton

November 6, 2019 @ 3:30 pm - 4:30 pm

Jianqing Fan Princeton University   Statistical Inference on Membership Profiles in Large Networks   Network data is prevalent in many contemporary big data applications in which a common interest is to unveil important latent links between different pairs of nodes. The nodes can be broadly defined such as individuals, economic entities, documents, or medical disorders in social, economic, text, or health networks.  Yet a simple question of how to precisely quantify the statistical uncertainty associated with the identification of latent…

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January 2020

STOR Colloquium: Anna Little, Michigan State University

January 8, 2020 @ 3:30 pm - 4:30 pm

Robust Statistical Procedures for Noisy, High-dimensional Data   This talk addresses two topics related to robust statistical procedures for analyzing noisy, high-dimensional data: (I) path-based spectral clustering and (II) robust multi-reference alignment. Both methods must overcome a large ambient dimension and lots of noise to extract the relevant low dimensional data structure in a computationally efficient way. In (I), the goal is to partition the data into meaningful groups, and this is achieved by a novel approach which combines a…

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STOR Colloquium: Yao Li, UC Davis

January 10, 2020 @ 3:30 pm - 4:30 pm

On the Robustness of Machine Learning Systems   Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in many machine learning tasks, including but not limited to image classification, text mining, and speech processing. However, recent studies have demonstrated the vulnerability of deep neural networks against adversarial examples, i.e., examples that are carefully crafted to fool a well-trained deep neural network while being indistinguishable from the natural images to humans.…

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STOR Colloquium: Paromita Dubey, UC-Davis

January 13, 2020 @ 3:30 pm - 4:30 pm

Fréchet Change Point Detection Change point detection is a popular tool for identifying locations in a data sequence where an abrupt change occurs in the data distribution and has been widely studied for Euclidean data. Modern data very often is non-Euclidean, for example distribution valued data or network data. Change point detection is a challenging problem when the underlying data space is a metric space where one does not have basic algebraic operations like addition of the data points and…

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STOR Colloquium: Yuqi Gu, University of Michigan

January 15, 2020 @ 3:30 pm - 4:30 pm

Uncover Hidden Fine-Grained Scientific Information: Structured Latent Attribute Models   In modern psychological and biomedical research with diagnostic purposes, scientists often formulate the key task as inferring the fine-grained latent information under structural constraints. These structural constraints usually come from the domain experts’ prior knowledge or insight. The emerging family of Structured Latent Attribute Models (SLAMs) accommodate these modeling needs and have received substantial attention in psychology, education, and epidemiology.  SLAMs bring exciting opportunities and unique challenges. In particular, with…

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STOR Colloquium: Lan Luo, University of Michigan

January 22, 2020 @ 3:30 pm - 4:30 pm

Lan Luo University of Michigan   Renewable Estimation and Incremental Inference in Streaming Data Analysis   New data collection and storage technologies have given rise to a new field of streaming data analytics, including real-time statistical methodology for online data analyses. Streaming data refers to high-throughput recordings with large volumes of observations gathered sequentially and perpetually over time. Such data collection scheme is pervasive not only in biomedical sciences such as mobile health, but also in other fields such as…

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STOR/Computational Med colloquium: Zhengwu Zhang, University of Rochester

January 24, 2020 @ 3:30 pm - 4:30 pm

Zhengwu Zhang University of Rochester Statistical Analysis of Brain Structural Connectomes   There have been remarkable advances in imaging technology, used routinely and pervasively in many human studies, that non-invasively measures human brain structure and function. Among them, a particular imaging modality called diffusion magnetic resonance imaging (dMRI) is used to infer shapes of millions of white matter fiber tracts that act as highways for neural activity and communication across the brain. The collection of interconnected fiber tracts is referred…

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STOR Colloquium: Eric Lock, University of Minnesota

January 31, 2020 @ 3:30 pm - 4:30 pm

Eric Lock University of Minnesota School of Public Health Bidimensional Linked Matrix Decomposition for Pan-Omics Pan-Cancer Analysis Several recent methods address the integrative dimension reduction and decomposition of linked high‐content data matrices. Typically, these methods consider one dimension, rows or columns, that is shared among the matrices. This shared dimension may represent common features measured for different sample sets (horizontal integration) or a common sample set with features from different platforms (vertical integration). This is limiting for data that take…

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