When: Friday, October 13 at 4:00 pm. Where: Fascitelli 040 Abstract: Data imputation is an essential preprocessing step in statistical learning that is to be performed before any technical analysis is conducted on partially observed data. Data originating from natural phenomena is low-rank due to diverse natural dependencies that a low-rank technique should primarily emphasize […]
Continue reading "Kelum Gajamannage, Low-rank data imputation using Hadamard deep autoencoders, with applications to fragmented trajectory reconstruction of collective motion"Category: Seminars
Antonios Argyriou, Passive Wireless Sensing: Implications on Privacy and Counter-Measures
When: Monday, October 30 at 1:00 pm. Where: Quinn 211. Who: Dr. Antonios Argyriou, Associate Professor, Department of Electrical and Computer Engineering, University of Thessaly, Greece. Abstract: Emitters of wireless signals are all around us 24/7. These wireless signals contain digital information that may be the target of different types of cyber security attacks. However, […]
Continue reading "Antonios Argyriou, Passive Wireless Sensing: Implications on Privacy and Counter-Measures"Ming-Hui Chen, A New Statistical Monitoring Approach Based on Linear Mixed-Effects Models: Application to Energy Usage Management on a Large University Campus
When: Friday, October 27th, from 4:00 PM to 5:00 PM Where: ENGR 040 Who: Professor Ming-Hui Chen, Department of Statistics, University of Connecticut Abstract: In this paper, we introduce a novel application of the linear mixed-effects model (LMM) repurposed for statistical monitoring. We develop an efficient EM algorithm to handle rapid estimation, especially in scenarios […]
Continue reading "Ming-Hui Chen, A New Statistical Monitoring Approach Based on Linear Mixed-Effects Models: Application to Energy Usage Management on a Large University Campus"ML Tlachac, Digital Mental Health Screening with Text Logs
When: Friday, September 29th, from 4:00 PM to 5:00 PM Where: ENGR 040 Who: ML Tlachac, Assistant Professor of Data Science at Bryant University Abstract: In this talk, ML Tlachac will provide an overview of digital mental health screening research with a focus on digital phenotyping data. The presentation will include insights into research involving […]
Continue reading "ML Tlachac, Digital Mental Health Screening with Text Logs"Basheer Qolomany, “The Role of Artificial Intelligence and Machine Learning in Complex Systems”
When: Friday, April 28, 2:00 PM Where: CBLS 100 Who: Basheer Qolomany, University of Nebraska at Kearney The Role of Artificial Intelligence and Machine Learning in Complex Systems Abstract: Current methods for diagnosing PAD require specialized vascular laboratory tests and do not allow natural environment detection, monitoring, or management of chronic PAD disease. The research […]
Continue reading "Basheer Qolomany, “The Role of Artificial Intelligence and Machine Learning in Complex Systems”"Shaun Wallace, “Human-Centric Systems for Quality Data”
When: Wednesday, April 19, 10:00 am Where: Ranger 208 Abstract: Information is often interpreted through tools, recently AI tools which sit closer to the user than the source information. From ChatGPT to instant answers on Google search and voice services on Siri and Alexa, these tools are only as good as their sources, especially for […]
Continue reading "Shaun Wallace, “Human-Centric Systems for Quality Data”"Haihan Yu, “A Composite Empirical Likelihood Method for Time Series in Frequency Domain Inference”
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”"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”"