Minisymposia Abstracts

Speaker: Jim Ramsey
McGill University
Title: TBA
Abstract: TBA

Speaker: Hulin Wu
University of Rochester
Title: Statistical Inverse Problem for High-Dimensional Dynamic Systems with Applications to Systems Biology Research
Abstract: Gene regulation is a complicated process. The interaction of many genes and their products forms an intricate biological network. Identification of this dynamic network will help us understand the biological process in a systematic way. However, the construction of such a dynamic network is very challenging for a high-dimensional system. We propose to use a set of ordinary differential equations (ODE), coupled with dimensional reduction by clustering and mixed-effects modeling techniques, to model the dynamic gene regulatory network (GRN). The ODE models allow us to quantify both positive and negative gene regulations as well as feedback effects of one set of genes in a functional module on the dynamic expression changes of the genes in another functional module, which results in a directed graph network. A six-step procedure, Screening, Clustering, Smoothing, regulation Identification, parameter Estimates refining and Function enrichment analysis (SCSIEF) is developed to identify the ODE-based dynamic GRN. In the proposed CSIEF procedure, a series of cutting-edge statistical methods and techniques are employed. We apply the proposed method to identify the dynamic GRN for yeast cell cycle progression data and immune response to influenza infection. We are able to annotate the identified modules through function enrichment analyses. Some interesting biological findings are discussed. The proposed procedure is a promising tool for constructing a general dynamic GRN and more complicated dynamic networks.

Speaker: Guang Cheng
Purdue University
Title: Inverse Problems in Semiparametric Statistical Models
Abstract: In this talk, we will give an overview on the semiparametric estimation in plain language. In particular, we will review an important concept ``Least Favorable Submodel" (LFS) in the semiparametric efficiency theory, and then relate the abstract LFS with different semiparametric estimation procedures, e.g., penalized estimation, used in practice. Four different semiparametric models will be discussed to illustrate the above interesting relation. In the end, we will review three popular semiparametric inferential tools, i.e., Bootstrap, Profile Sampler and Sieve Estimation.

Speaker: Yalchin Efendiev
Texas A&M University
Title: Bayesian uncertainty quantification for channelized subsurface characterization
Abstract: Uncertainties in the spatial distribution of subsurface properties play a significant role in predicting the fluid flow behavior in heterogeneous media. To quantify the uncertainties in flow and transport processes in heterogeneous porous formations, complex dynamic and static data need to be reconciled with stochastic description of subsurface properties. The dynamic data measure the flow and transport responses that are largely affected by the spatial distribution of distinct geologic facies with sharp contrasts in properties across facies boundaries. Subsurface systems represent a challenging example in which the orientation and geometric shape of the channels dominate the field scale flow behavior in the subsurface. Most existing approaches have been limited to modeling the channel boundaries with simplified (e.g., sinusoidal) functions.

In subsurface characterization, facies features need to be properly accounted when constructing prior models for subsurface properties. Furthermore, stochastic conditioning of facies distributions to nonlinear dynamic flow data presents a major challenge in underground fluid flow prediction. In this talk, hierarchical Bayesian approaches will be discussed that use level set methods for facies deformation to model facies boundaries. These prior models can be used with fast multiscale forward simulation tools and parallel multi-stage sampling techniques to explore the uncertainties in the geologic facies description and the resulting flow predictions.

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