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"
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”"
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”"
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”"
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”"
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”"
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”"
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”"
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”"
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”"