Modern data collection in bioinformatics and other big-data paradigms often incorporates traits derived from multiple different points of view of the observations. We call this data multi-view data or multi-block data. The field of data integration develops and applies new … Read more
Mark He Community Detection in Multimodal Networks Community detection on networks is a basic, yet powerful and ever-expanding set of methodologies that is useful in a variety of settings. This dissertation discusses a range of different community detection on networks … Read more
Xi Yang Machine Learning Methods in HDLSS Settings During the exploration of high dimension-low-sample-size (HDLSS) data in different fields such as genetics, finance, computer science, etc, various machine learning methods have been developed. This dissertation includes the invention of novel … Read more
A Glimpse into Random Geometry: from Brownian motion to Gaussian Free Field For "random curve", a natural and classical model is Brownian motion; when it comes to "random surface", a promising candidate model is Gaussian free field (GFF), which can … Read more
Generalized Fiducial Inference on Differentiable Manifolds I’ll discuss the problem of defining a general fiducial density on an implicitly defined differentiable manifold and introduce our recent solution. Our proposed density extends the usual generalized fiducial distribution (GFD) by projecting the … Read more
Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages The Vector AutoRegressive Moving Average (VARMA) model is fundamental to the theory of multivariate time series; however, identifiability issues have led practitioners to abandon it in favor of the simpler … Read more
Stefanos Kechagias Analytical Consulting & Enterprise Solutions, SAS Institute Scratch out “learn TensorFlow” from your New Year bucket list. In goes “learn how to tell valuable stories” In this talk we will discuss three Story Telling Mediums statisticians employ to … Read more
Dmitriy Drusvyatskiy University of Washington at Seattle Stochastic methods for nonsmooth nonconvex optimization Stochastic iterative methods lie at the core of large-scale optimization and its modern applications to data science. Though such algorithms are routinely and successfully used in practice … Read more
Samopriya Basu Fiducial inference for SDEs with constant diffusion In this talk, I will talk about my research with my advisor Prof. Jan Hannig on carrying out fiducial inference for stochastic ordinary differential equations with constant diffusion coëfficient. The diffusion … Read more
Michael Conroy UNC-Chapel Hill Efficient rare-event simulation for branching processes In this talk I’ll discuss some of my past, current, and future work with importance sampling schemes for maxima of branching processes. In a recent paper, my collaborators and I … Read more
