Upcoming Events

  • 2019 Jan 28

    Subhabrata Sen: Job Talk

    12:00pm to 1:00pm

    Location: 

    Science Center 300H

    Network data: Modeling and Statistical Analysis

    Network data arises frequently in modern scientific applications. These networks often exhibit specific characteristics like edge sparsity, heavy-tailed degree distribution etc. Some broad challenges arising in the analysis of such datasets include (i) developing flexible, interpretable models for networks, (ii) provably recovering latent structure from such data, and (iii) testing for goodness of fit.

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    Read more about Subhabrata Sen: Job Talk
  • 2019 Jan 31

    Jonathan Weed: Job Talk

    12:00pm to 1:00pm

    Location: 

    Science Center 300H

    Large-scale Optimal Transport: Statistics and Computation

    Optimal transport is a concept from probability which has recently seen an explosion of interest in machine learning and statistics as a tool for analyzing high-dimensional data. However, the key obstacle in using optimal transport in practice has been its high statistical and computational cost. In this talk, we show how exploiting different notions of structure can lead to better statistical rates—beating the curse of dimensionality—and state-of-the-art algorithms.

  • 2019 Feb 01

    Qingyuan Zhao: Job Talk

    12:00pm to 1:00pm

    Location: 

    Science Center 300H

    Mendelian randomization: A comprehensive statistical approach and applications to preventing heart disease

    Mendelian randomization (MR) can give unbiased estimate of a confounded causal effect by using genetic variants as instrumental variables. The summary-data MR design is rapidly gaining popularity in practice due to the increasing availability of large-scale genome-wide association studies. As we are entering the "MR of every risk factor on every disease outcome" era, existing statistical methods still have several major limitations and lack theoretical...

    Read more about Qingyuan Zhao: Job Talk
  • 2019 Feb 04

    Fei Gao: Job Talk

    12:00pm to 1:00pm

    Location: 

    Science Center 300H

    Non-iterative Estimation Update for Parametric and Semiparametric Models with Population-based Auxiliary Information

    With the advancement in disease registries and surveillance data, population-based information on disease incidence, survival probability or other important biological characteristics become increasingly available. Such information can be leveraged in studies that collect detailed measurements but with smaller sample sizes. In contrast to recent proposals that formulate the...

    Read more about Fei Gao: Job Talk
  • 2019 Feb 05

    Yiqiao (Joe) Zhong: Job Talk

    12:00pm to 1:00pm

    Location: 

    Science Center 300H

    Spectral Methods and Nonconvex Optimization: A Modern Statistical Perspective

    Modern statistical analysis often requires integration of statistical thinking and algorithmic thinking. In many problems, statistically sound estimation procedures (e.g., the MLE) may be difficult to compute, at least in the naive form. This challenge calls for a new look into simple statistical methods such as the spectral methods (including PCA), as well as an examination of optimization algorithms from the statistical lens.

    In this talk, I will sample two typical...

    Read more about Yiqiao (Joe) Zhong: Job Talk
  • 2019 Feb 07

    Pragya Sur: Job Talk

    12:00pm to 1:00pm

    Location: 

    Science Center 300H

    Title: A modern maximum-likelihood approach for high-dimensional logistic regression

    Abstract: 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...

    Read more about Pragya Sur: Job Talk
  • 2019 Feb 08

    Yuansi Chen: Job Talk

    12:00pm to 1:00pm

    Location: 

    Science Center 300H

    Stability-driven deep model interpretation and provably fast MCMC sampling

    Data science is transforming many traditional ways in which we approach scientific problems. While the abundance of data and algorithms generate a lot of excitement in statistical modeling, serious concerns about how to reliably and efficiently extract scientific knowledge from data and models are being raised.

    In this talk, I will address particular reliability and efficiency issues that arise from my PhD study on a neuroscience project. Understanding how primates process...

    Read more about Yuansi Chen: Job Talk