Fall 2019

Speaker: Chris Taylor
Affiliation: Department of Microbiology, Immunology and Parasitology, LSUHSC School of Medicine, New Orleans – LA
Title: Assessing the vaginal microbiome and interactions with sexually transmitted infections
Date and Time: August 26th, 2019, 3:00 pm
Room: LEC, Room 303
Abstract: Changes in the composition of the vaginal microbiome have been associated with sexually transmitted infections and reproductive health issues. I will present results from three clinical studies using 16S rRNA gene sequencing that examine sharing of microbiota among sexual partners, changes in the vaginal microbiota over time, and relationships with bacterial vaginosis and chlamydia trachomatis.

Speaker: Yusha Liu
Affiliation: Statistics Department, Rice University, Houston – TX
Title: Function-on-Scalar Quantile Regression with Application to Mass Spectrometry Proteomics Data
Date and Time: September 16, 2019, 3:00 pm
Room: LEC, Room 303
Abstract: Mass spectrometry proteomics, characterized by spiky, spatially heterogeneous functional data, can be used to identify potential cancer biomarkers. Existing mass spectrometry analyses utilize mean regression to detect spectral regions that are differentially expressed across groups. However, given the inter-patient heterogeneity that is a key hallmark of cancer, many biomarkers are only present at aberrant levels for a subset of, not all, cancer samples. Differences in these biomarkers can easily be missed by mean regression, but might be more easily detected by quantile-based approaches. Thus, we propose a unified Bayesian framework to perform quantile regression on functional responses. Our approach utilizes an asymmetric Laplace working likelihood, represents the functional coefficients with basis representations which enable borrowing of strength from nearby locations, and places a global-local shrinkage prior on the basis coefficients to achieve adaptive regularization. Different types of basis transform and continuous shrinkage priors can be used in our framework. An efficient Gibbs sampler is developed to generate posterior samples that can be used to perform Bayesian estimation and inference while accounting for multiple testing. Our framework performs quantile regression and coefficient regularization in a unified manner, allowing them to inform each other and leading to improvement in performance over competing methods as demonstrated by simulation studies. We apply this model to identify proteomic biomarkers of pancreatic cancer missed by previous mean-regression based approaches. Supplementary materials for this article are available online. 

Speaker: Zack Belou
Affiliation: DXC Technology
Title: Enterprise Applications of Machine Learning in Fault Detection & Autonomous Vehicles
Date and Time: September 23, 3:00 pm
Room: LEC, Room 303
Abstract: As businesses collect more data than ever, sophisticated data pipelines and algorithms provide key advantages for innovative companies. From telecommunications providers that handle the world’s internet traffic to automotive manufacturers trying to perfect self-driving technology, every business is looking for the advantages machine learning and AI promise. In this talk, we will dive into two data science use cases that break down the challenges many enterprises face in the world of Big Data/machine learning, and how data scientists and engineers solve them. We will breakdown how businesses manage the data lifecycle, organize non-structured data, and develop machine learning algorithms in practice. We will also take a comprehensive look at Phased LSTM recurrent networks, Graph database processing, and image classification and their implementation in commercial environments.

Speaker: Kalimuthu Krishnamoorthy, PhD
Affiliation: Mathematics Department, University of Louisiana Lafayette, Lafayette – LA
Title: —-
Date and Time: September 30th, 2019, 3:00 pm
Room: LEC, Room 303 

Speaker: Zhuolin Qu, PhD
Affiliation: Mathematics Department, Tulane University, New Orleans – LA
Title: Network Modeling the Impact of Community-based Male-screening on the Chlamydia Trachomatis Prevalence in Women
Date and Time: October 14, 2019, 3:00 pm
Room: LEC, Room 303
Abstract: We create and analyze a stochastic network-based model to understand the control of Chlamydia trachomatis (Ct) among young African American (AA) in New Orleans. Ct is the most commonly reported bacterial sexually transmitted infection in the United States and is a major cause of infertility, pelvic inflammatory disease, and ectopic pregnancy among women. Despite decades of screening women for Ct, the rates continue to increase among young AA compared to other groups. The community-based program “Check It” proposes that men are an important reservoir of infection for women and screening AA men could make an impact on the rates among women. To quantify the effectiveness of the male-screening strategy, we propose an agent-based network model to simulate a realistic sexual contact network for assortative mixing among the targeted population. We model both the existing intervention for women through the annual exam and the “Check It” male-screening based intervention through venue-based enrollment. The model accounts for various intervention strategies implemented in the program, including the expedited index treatment, expedited partner treatment, social network peer referral, and rescreening. We use sensitivity analysis to quantify the significance of each intervention component onto the prevalence in women. 

Speaker: Gustavo Didier
Affiliation: Mathematics Department, Tulane University, New Orleans – LA
Title: —-
Date and Time: October 14, 2019, 3:00 pm
Room: LEC, Room 303

Speaker: Xueyan Sherry Liu, PhD
Affiliation: Mathematics Department, University of New Orleans, New Orleans – LA
Title: A novel statistical tool for analyzing colcoalization in super-resolution images.
Date and Time: October 21st, 2019, 3:00 pm
Room: LEC, Room 303
Abstract: Spatial data from high-resolution images abound in many scientific disciplines. For example, single-molecule localization microscopy, such as stochastic optical reconstruction microscopy (STORM), provides super-resolution images to help scientists investigate co-localization of proteins and hence their interactions inside cells, which are key events in living cells. However, there are few accurate methods for analyzing co-localization in super-resolution images. The current methods and software are prone to produce false positive errors, and are restricted to only 2-dimensional images. In this study we develop a novel statistical method, along with an R package and on-line apps, to effectively address the problems of unbiased and robust quantification and comparison of protein co-localization for multiple 2- and 3-dimensional image datasets. This method significantly improves the analysis of protein co-localization using super-resolution image data, as shown by its excellent performance in simulation studies and an analysis of LC3-LAMP1 protein co-localization in cell autophagy. 

Speaker: Karl Mahlburg
Affiliation: Mathematics Department, LSU, Baton Rouge – LA
Title: —-
Date and Time: October 28th, 2019, 3:00 pm
Room: LEC, Room 303

Speaker: Luis Escobar
Affiliation: Experimental Statistics Department, LSU, Baton Rouge – LA
Title: —-
Date and Time: November 18th, 2019, 3:00 pm
Room: LEC, Room 303

Organizer: A. Chapple, PhD

Spring 2019 Archive
Fall 2018 Archive