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November 2018

STOR Colloquium: Weijie Su, UPenn

November 28, 2018 @ 2:30 pm - 3:30 pm

STOR Colloquium Wednesday, November 28th, 2018 120 Hanes Hall 3:30pm Weijie Su University of Pennsylvania Uncertainty Quantification for Stochastic Gradient Descent   Stochastic gradient descent (SGD) is an immensely popular approach for online learning in settings where data arrives in a stream or data sizes are very large. However, despite an ever-increasing volume of work on SGD, much less is known about the statistical inferential properties of SGD-based predictions. Taking a fully inferential viewpoint, this talk introduces a novel procedure…

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December 2018

STOR Colloquium: Ana-Maria Staicu, NC State

December 3, 2018 @ 2:30 pm - 3:30 pm

STOR Colloquium Monday, December 3rd, 2018 120 Hanes Hall 3:30pm Ana-Maria Staicu North Carolina State University Longitudinal Dynamic Functional Regression   In this talk we discuss regression models to study the association between scalar outcomes and functional predictors observed over time, at many instances, in longitudinal studies. We propose a parsimonious modeling framework to study time-varying regression that leads to superior prediction properties and allows to reconstruct full trajectories of the response. The idea is to model the time-varying functional…

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January 2019

STOR Colloquium: Bikram Karmakar, University of Pennsylvania

January 11, 2019 @ 2:30 pm - 3:30 pm

Bikram Karmakar Statistics Department, The Wharton School, University of Pennsylvania Evidence factors for observational studies: methodology, computation and applications.   Observational studies aim to elucidate cause-and-effect relationships from situations in which treatment is not randomly assigned. A sensitivity analysis for an observational study assesses how much bias, due to non-random assignment of treatment, would be necessary to change the conclusions of an analysis that assumes treatment assignment was effectively random. Causal conclusions gain strength from a demonstration that they are…

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STOR Colloquium: Joshua Cape, Johns Hopkins

January 14, 2019 @ 2:30 pm - 3:30 pm

Joshua Cape Johns Hopkins University   Statistical analysis and spectral methods for signal-plus-noise matrix models   Estimating eigenvectors and principal subspaces is of fundamental importance for numerous problems in statistics, data science, and network analysis, including covariance matrix estimation, principal component analysis, and community detection. For each of these problems, we obtain foundational results that precisely quantify the local (e.g., entrywise) behavior of sample eigenvectors within the context of a unified signal-plus-noise matrix framework. Our methods and results collectively address…

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STOR Colloquium: Yanglei Song, University of Illinios

January 16, 2019 @ 2:30 pm - 3:30 pm

Yanglei Song University of Illinois at Urbana-Champaign  Asymptotically optimal multiple testing with streaming data   The problem of testing multiple hypotheses with streaming (sequential) data arises in diverse applications such as multi-channel signal processing, surveillance systems, multi-endpoint clinical trials, and online surveys. In this talk, we investigate the problem under two generalized error metrics. Under the first one, the probability of at least k mistakes, of any kind, is controlled. Under the second, the probabilities of at least k1 false…

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STOR Colloquium: Vince Lyzinski, Univ. of Massachusetts Amherst

January 18, 2019 @ 2:30 pm - 3:30 pm

Vince Lyzinski The University of Massachusetts Amherst   Graph matching in edge-independent networks   The graph matching problem seeks to find an alignment between the vertex sets of two graphs that best preserves common structure across graphs. Here, we consider the closely related problem of graph matchability: Given a latent alignment between the vertex sets of two graphs, under what conditions will the solution to the graph matching optimization problem recover this alignment in the presence of shuffled vertex labels? …

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STOR Colloquium: Jingshen Wang, University of Michigan

January 23, 2019 @ 2:30 pm - 3:30 pm

Jingshen Wang  University of Michigan   Inference on Treatment Effects after Model Selection   Inferring cause-effect relationships between variables is of primary importance in many sciences. In this talk, I will discuss two approaches for making valid inference on treatment effects when a large number of covariates are present. The first approach is to perform model selection and then to deliver inference based on the selected model. If the inference is made ignoring the randomness of the model selection process,…

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STOR Colloquium: Geoffrey Schiebinger, MIT and Harvard

January 25, 2019 @ 2:30 pm - 3:30 pm

Geoffrey Schiebinger The Broad Institute of MIT and Harvard and the MIT Statistics and Data Science Center Towards a mathematical theory of development   In this talk we introduce a mathematical model to describe temporal processes like embryonic development and cellular reprogramming. We consider stochastic processes in gene expression space to represent developing populations of cells, and we use optimal transport to recover the temporal couplings of the process. We apply these ideas to study 315,000 single-cell RNA-sequencing profiles collected…

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STOR Colloquium: Jeffrey Regier, Univ. of California, Berkeley

January 28, 2019 @ 2:30 pm - 3:30 pm

Jeffrey Regier University of California, Berkeley Statistical Inference for Cataloging the Visible Universe     A key task in astronomy is to locate astronomical objects in images and to characterize them according to physical parameters such as brightness, color, and morphology. This task, known as cataloging, is challenging for several reasons: many astronomical objects are much dimmer than the sky background, labeled data is generally unavailable, overlapping astronomical objects must be resolved collectively, and the datasets are enormous -- terabytes now,…

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STOR Colloquium: Pragya Sur, Stanford University

January 30, 2019 @ 2:30 pm - 3:30 pm

Pragya Sur  Stanford University   A modern maximum-likelihood approach for high-dimensional logistic regression   Logistic regression is arguably the most widely used and studied non-linear model in statistics. Classical maximum-likelihood theory based statistical inference is ubiquitous in this context. This theory hinges on well-known fundamental results: (1) the maximum-likelihood-estimate (MLE) is asymptotically unbiased and normally distributed, (2) its variability can be quantified via the inverse Fisher information, and (3) the likelihood-ratio-test (LRT) is asymptotically a Chi-Squared. In this talk, I…

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