Statistics Seminars Series

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Brown Statistics Seminars features talks on current developments in statistical methodology and is open to the entire Brown community. The Seminars are hosted collaboratively by the Department of Biostatistics and the Center for Statistical Sciences to provide educational and research opportunities to graduate, undergraduate, and medical students as well as to researchers across the University.  The seminars take place on selected Mondays throughout the academic year and feature leading researchers from the US and internationally.  The series is sponsored by The Center for Statistical Sciences, The Department of Biostatistics, and the Charles K. Colver Lectureships & Publications. 

To receive our seminar announcements, contact us.

  • Sudipto Banerjee, PhD
    Mar
    6
    12:00pm - 1:00pm

    Statistics Seminar and Charles K. Colver Lectureship Series | Sudipto Banerjee, Ph.D.

    School of Public Health at Brown University, 121 south Main Street, Providence, RI 02912, Room 245

    Sudipto Banerjee, Ph.D.,
    Professor and Chair
    UCLA Department of Biostatistics

     

    Talk Title: On Massively Scalable Spatial Process Models for High-Resolution Actigraph Data

    Abstract: Rapid developments in streaming data technologies have enabled real-time monitoring of human activity. Wearable devices, such as wrist-worn sensors that monitor gross motor activity (actigraphy), have become prevalent. An actigraph unit continually records the activity level of an individual, producing large amounts of high-resolution measurements that can be immediately downloaded and analyzed. While this type of BIG DATA includes both spatial and temporal information, we argue that the underlying process is more appropriately modeled as a stochastic evolution through time, while accounting for spatial information separately. A key challenge is the construction of valid stochastic processes over paths. We devise a spatial-temporal modeling framework for massive amounts of actigraphy data, while delivering fully model-based inference and uncertainty quantification. Building upon recent developments in scalable inference, we construct temporal processes using directed acyclic graphs (DAG) and develop optimized implementations of collapsed Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference. We test and validate our methods on simulated data and subsequently apply and verify their predictive ability on an original dataset from the Physical Activity through Sustainable Transport Approaches (PASTA-LA) study conducted by UCLA’s Fielding School of Public Health.

    *Light refreshments will be served

    Biology, Medicine, Public Health, BioStatsSeminar, Research
  • Mahlet G. Tadesse, Ph.D.
    Mar
    13
    12:00pm - 1:00pm

    Statistics Seminar and Charles K. Colver Lectureship Series | Mahlet G. Tadesse, Ph.D.

    School of Public Health at Brown University, 121 south Main Street, Providence, RI 02912, Room 245

    Mahlet G. Tadesse, Ph.D.,
    Professor and Chair
    Department of Mathematics & Statistics
    Georgetown University

     

    Talk Title: Variable selection in mixture models: Uncovering cluster structures and relevant features

    Abstract: Identifying latent classes and component-specific relevant predictors can shed important insights when analyzing high-dimensional data. In this talk, I will present methods we have proposed to address this problem in a unified manner by combining ideas of mixture models and variable selection. In particular, I will discuss (1) an integrative model to relate two high-dimensional datasets by fitting multivariate mixture of regression models using stochastic partitioning, and (2) a mixture of regression trees approach to uncover homogeneous subgroups of observations and their associated predictors accounting for non-linear relationships and interaction effects. I will illustrate the methods with genomic applications.

    *Light refreshments will be served

    Biology, Medicine, Public Health, BioStatsSeminar, Research
  • Johannes Lederer PhD
    Apr
    17
    12:00pm - 1:00pm

    Statistics Seminar | Johannes Lederer, Ph.D.

    School of Public Health, 121 South Main Street, Room 245

    Talk Title: Sparse Deep Learning

    Abstract: Sparsity is popular in statistics and machine learning, because it can avoid overfitting, speed up computations, and facilitate interpretations. In deep learning, however, the full potential of sparsity still needs to be explored. This presentation first recaps sparsity in the framework of high-dimensional statistics and then introduces sparsity-inducing methods and corresponding theory for modern deep-learning pipelines.

    Biology, Medicine, Public Health, BioStatsSeminar, Research