- Marco Alvarez, Transforming Research and Higher Education with Generative AI and Foundation Models (4/4/2024) - When: Friday April 5. – noon-1 p.m. Where: Bliss 190 This talk delves into the transformative potential of generative AI and foundation models in both scientific research and higher education. Foundation models represent a seismic shift in AI capabilities, empowering researchers to analyze data, generate hypotheses, and uncover knowledge with unprecedented efficiency. Trained on vast […]
- Yuwen Gu, fastkqr: A Fast Algorithm for Kernel Quantile Regression (3/20/2024) - When: Friday, March 22, from 2:00 PM to 3:00 PM Where: ENGR 045 Abstract: Quantile regression is a powerful tool for robust and heterogeneous learning that has seen applications in a diverse range of applied areas. Its broader application, however, is often hindered by the substantial computational demands arising from the nonsmooth quantile loss function. […]
- Caiwen Ding, Co-Designing Algorithms and Hardware for Efficient Machine Learning (ML): Advancing the Democratization of ML (3/6/2024) - When: Friday, March 8th, from 2:00 PM to 3:00 PM Where: ENGR 045 Abstract: The rapid deployment of ML has witnessed various challenges such as prolonged computation and high memory footprint on systems. In this talk, we will present several ML acceleration frameworks through algorithm-hardware co-design on various computing platforms. The first part presents a […]
- Oana Ignat, Towards Language-Vision Models for Positive Societal Impact (2/16/2024) - When: Monday 2/19 from 1:00 to 2:00 PM Where: Tyler 055 Abstract: Solving complex real-world problems often requires AI models that can process information from multiple modalities, such as language and vision, which can align with the needs of people from diverse backgrounds. An effective AI model will not only learn how to interact with […]
- Kaleel Mahmood, On the Robustness of Vision Transformers to Adversarial Examples (2/14/2024) - When: Friday 2/16 from 10:00 to 11:00 AM Where: Ranger 202 Abstract: Machine learning has become ubiquitous, being deployed in a range for domains like self driving cars, medical imaging and face recognition. With this increased use of machine learning an important question arises, how secure are these systems? In this presentation we dive into […]
- Taslima Akter, Designing Privacy Enhancing Technology for Blind and Low-Vision (BLV) People (2/8/2024) - WHen: Tuesday 2/13 from 1:00 to 2:00 PM. Where: Quinn 214 Abstract: Advancements in computer vision and machine learning have empowered Blind and Low-Vision (BLV) individuals through camera-based assistive applications. These systems, capable of recognizing objects, identifying colors, and reading text, provide independence to BLV users. However, the reliance on camera-based assistive systems introduces privacy […]
- Andrew Gallant, Engineering a Fast Grep (2/5/2024) - When: Tuesday February 27, 4PM Where: Kirk Auditorium Abstract: Grep is a command line tool for searching the contents of files for a regular expression and printing lines that match. Tools like grep are commonly used to search plain text such as log files and code repositories in an ad hoc manner. As the size […]
- Gianluca Brero (Brown University), Stackelberg POMDP: A Reinforcement Learning Approach for Economic Design (1/30/2024) - When: Friday, Feb 2nd, from 2:00 PM to 3:00 PM Where: ENGR 045 Abstract: We introduce a reinforcement learning framework for economic platform design where the interaction between the platform designer and the participants is modeled as a Stackelberg game. In this game, the designer (leader) sets up the rules for the platform, while the […]
- Kelum Gajamannage, Low-rank data imputation using Hadamard deep autoencoders, with applications to fragmented trajectory reconstruction of collective motion (10/9/2023) - 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 […]
- Antonios Argyriou, Passive Wireless Sensing: Implications on Privacy and Counter-Measures (10/2/2023) - 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, […]
- Ming-Hui Chen, A New Statistical Monitoring Approach Based on Linear Mixed-Effects Models: Application to Energy Usage Management on a Large University Campus (10/2/2023) - 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 […]
- ML Tlachac, Digital Mental Health Screening with Text Logs (9/26/2023) - 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 […]
- Lily Sisouvong, “Pair Programming with Random Partners” (7/31/2023) - CSC Thesis Defense When: Monday, July 31, 4:00 pm Tyler Hall Room 052 Pair programming is a technique within the computer science space in which two programmers are paired on one computer to solve a related programming task. This technique is often practiced in both the industry and the academic setting, as it has a […]
- Basheer Qolomany, “The Role of Artificial Intelligence and Machine Learning in Complex Systems” (4/24/2023) - 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 […]
- Shaun Wallace, “Human-Centric Systems for Quality Data” (4/17/2023) - 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 […]
- New England Computer Science Teachers Association Conference (3/21/2023) - The New England chapter of the Computer Science Teachers Association (CSTA) is coming to UConn at Storrs on October 20, 2023. Educators of all grade levels, including post-secondary, education leaders, library media specialists, administrators, coaches, school counselors, and researchers are welcome to join in the fun! We will focus on BUILDING CS PATHWAYS. Keynote speakers […]
- Haihan Yu, “A Composite Empirical Likelihood Method for Time Series in Frequency Domain Inference” (2/24/2023) - 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: […]
- Matthew Wascher, “Monitoring disease prevalence and transmission in a population under repeated testing” (2/24/2023) - 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 […]
- Xihao Li, “Statistical methods for integrative analysis of large-scale whole-genome sequencing studies” (2/24/2023) - 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 […]
- Fei Dou, “Machine Intelligence of Ubiquitous Computing in the Internet of Things” (2/17/2023) - 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 […]
- Xuhao Chen, “Domain Specific Computing on Graphs” (2/17/2023) - 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. […]
- Machine Learning Workshop Series (Spring 2023) (2/14/2023) - Hands-on workshops covering some introductory Machine Learning tools and techniques. Python Tools for Machine Learning – February 10th Classes in Python, Deep Learning Frameworks – February 24th Training and Fine Tuning Models for Downstream Tasks – March 24th Reporting Your Findings in LaTeX – April 14th February 10th, Python Tools for Machine Learning The first […]
- Sidi Lu, “Toward Reliable, Scalable, and Efficient Edge-enabled Applications for Connected Vehicles” (2/7/2023) - 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 […]
- Computer Science Diversity Equity and Inclusion Graduate Assistant (CS DEI GA) (2/2/2023) - Position Title: Computer Science Diversity Equity and Inclusion Graduate Assistant (CS DEI GA) Role Summary: This position provides overall Diversity, Equity and Inclusion support for the Department of Computer Science and Statistics. In particular, the work involves participating in four specific projects: Pathways to Success in Computer Science (P2SinCS), IGT Scholarships for Women in Computer […]
- Tenure Track Faculty Position in Statistics (11/2/2022) - University of Rhode Island Department of Computer Science and Statistics The Department of Computer Science and Statistics in the College of Arts and Sciences (A&S) at the University of Rhode Island invites applications for a tenure-track Assistant Professor of Statistics position with appointment to begin the academic year 2023-2024. DUTIES AND RESPONSIBILITIES: The […]
- Tenure-Track Faculty Position in Computer Science (10/17/2022) - The University of Rhode Island invites applications for a tenure-track Assistant Professor in the Department of Computer Science and Statistics. We seek candidates who can contribute to both teaching and research in computer science. Of particular interest are candidates with expertise in the broad area of computer systems and its application across multiple disciplines. The […]
