When: Mar 3rd, 11:00-12:00 Where: Tyler 053 Zoom link: https://uri-edu.zoom.us/my/guangyuzhu Abstract: Frequency domain analysis of time series is often difficult, as periodogram-based statistics involve non-linear averages with complicated variances. Due to the latter, nonparametric approximations from resampling or empirical likelihood (EL) are useful. However, current versions of periodogram-based EL for time series are highly restricted: […]
Continue reading "Haihan Yu, “A Composite Empirical Likelihood Method for Time Series in Frequency Domain Inference”"Category: Seminars
Matthew Wascher, “Monitoring disease prevalence and transmission in a population under repeated testing”
When: Mar 2nd, 14:00-15:00 Where: Tyler 053, Zoom link: https://uri-edu.zoom.us/my/guangyuzhu Abstract: In this talk, I will describe a statistical methodology developed as part of the COVID-19 monitoring efforts of The Ohio State University (OSU) and which is designed for monitoring disease transmission using repeated testing data. Under a repeated testing scheme in which individuals who […]
Continue reading "Matthew Wascher, “Monitoring disease prevalence and transmission in a population under repeated testing”"Xihao Li, “Statistical methods for integrative analysis of large-scale whole-genome sequencing studies”
When: Feb 28th, 13:30-14:30 Where: Tyler 049 Abstract: Large-scale whole genome sequencing (WGS) studies have enabled the analysis of rare variants (RVs) associated with complex human traits. There are several challenges in analyzing WGS data, including computation scalability, limited scope to integrate variant biological functions, and lack of ability to leverage summary statistics across multiple […]
Continue reading "Xihao Li, “Statistical methods for integrative analysis of large-scale whole-genome sequencing studies”"Fei Dou, “Machine Intelligence of Ubiquitous Computing in the Internet of Things”
When: Friday, February 24th, 1:00-2:00PM Where: Beaupre 105 Abstract: The penetration of technologies such as Machine Learning (ML), Artificial Intelligence (AI), wireless broadband, and the Internet of Things (IoT) is propelling the rapid adoption of ubiquitous devices across a variety of sectors. However, the enhancement of machine intelligence in ubiquitous computing in the IoT is […]
Continue reading "Fei Dou, “Machine Intelligence of Ubiquitous Computing in the Internet of Things”"Xuhao Chen, “Domain Specific Computing on Graphs”
When: Thursday, February 23rd, 1:30-2:30PM Where: Woodward 216 Abstract: Numerous applications in social networks, e-commerce, biomedicine and security, are driven by graph algorithms. The graph data is massive and sparse, which poses great challenges in computing system design. In this talk, I will discuss the approach known as domain specific computing, to address this challenge. […]
Continue reading "Xuhao Chen, “Domain Specific Computing on Graphs”"Sidi Lu, “Toward Reliable, Scalable, and Efficient Edge-enabled Applications for Connected Vehicles”
When: February 13, 2023, 1-2 pm Where: Beaupre 105 As a mobile sensing, computing, communication, and energy storage platform, connected vehicle is transforming from the vehicle-centric, closed, fixed-function vehicle to the AI-centric, connected, and software-defined vehicle that enables vehicle-to-everything and vehicle-to-grid. However, this evolution brings a series of technical challenges across diverse areas (e.g., inference […]
Continue reading "Sidi Lu, “Toward Reliable, Scalable, and Efficient Edge-enabled Applications for Connected Vehicles”"Dr. Enrique ter Horst, “A Bayesian time varying approach to risk neutral density estimation”
Who: Dr. Enrique ter Horst, Universidad de los Andes, Bogotá, Colombia What: “A Bayesian time varying approach to risk neutral density estimation.” When: Friday November 4th, 4PM Where: Beaupre 105 Abstract: We expand the literature of risk neutral density estimation across maturities from implied volatility curves, which are usually estimated and interpolated through cubic smoothing […]
Continue reading "Dr. Enrique ter Horst, “A Bayesian time varying approach to risk neutral density estimation”"Dr. Noah M. Daniels, URI – Manifold Mapping Enables Fast Search, Anomaly Detection, and More
Join us for our Computer Science and Statistics Colloquium Series. When: Friday, Oct 28th, 4PM Where: Beaupre 105 Speaker: Dr. Noah M. Daniels, URI Title: Manifold Mapping Enables Fast Search, Anomaly Detection, and More Most likely, you have heard the term “Big Data.” The world has been experiencing an explosive growth in data, and that […]
Continue reading "Dr. Noah M. Daniels, URI – Manifold Mapping Enables Fast Search, Anomaly Detection, and More"Dr. Lorin Crawford, Microsoft Research New England and Brown University
Variable Selection and Prioritization in Bayesian Machine Learning Methods Where: Beaupre 105 When: Friday, October 21st, 4PM Abstract: A consistent theme of the work done in my lab group is to take modern computational approaches and develop theory that enable their interpretations to be related back to classical genomic principles. The central aim of this […]
Continue reading "Dr. Lorin Crawford, Microsoft Research New England and Brown University"Dr. Roberta De Vito, Brown University
Multi-Study Approaches: adventures from nutrition to genomics Date: September 30th, 2022 @ 4pm-5pm Location: Beaupre 105 Host: Gavino Puggione Biostatistics and computational biology are increasingly facing the urgent challenge of efficiently dealing with a large amount of experimental data. High-throughput assays are transforming the study of biology, as they generate a rich, complex, and diverse […]
Continue reading "Dr. Roberta De Vito, Brown University"