CCR AI/ML Seminar: Machine Learning Approaches for Characterizing Global Sea Surface Temperature Fields

The Center for Computational Research is planning a series of monthly talks on AI/ML techniques and applications across various scientific domains. We envision the talks to strike a good balance between depth and breath. The goal of these talks will be to (i) introduce the particular AI/ML technique to fellow faculty and graduate students who have a basic understanding of deep learning and (ii) present a variety of applications in different domains without assuming deep domain knowledge. Each event will start with a 45 minute talk with 15 minutes for questions and a subsequent 30 minute networking session for brainstorming and further discussion. The speakers will be URI faculty from a number of colleges.

Details on the first talk appear below.

Speaker: Peter Cornillon (GSO)
Date/Time/Location: May 1st, 3pm, 112 East Hall.
Title: Machine Learning Approaches for Characterizing Global Sea Surface Temperature Fields
Abstract: Sea surface temperature (SST) fields derived from satellite-borne sensors offer an ideal dataset for exploration using machine learning techniques. In this presentation, I will describe the use of an auto-encoder, in combination with a flow equalization technique, to identify outliers in a 20-year, global, twice-daily archive of SST fields. I will then demonstrate how this same approach can be applied to evaluate the performance of a global ocean circulation model.
Switching gears, I will introduce a machine learning model based on contrastive learning applied to the same SST datasets—this time to uncover and categorize fundamental spatial patterns within the fields. Finally, I will briefly touch on an analysis of the latent space produced by the contrastive learning model, with a focus on estimating the intrinsic dimensionality of SST field variability.

 

World Quantum Day 2025

URI celebrates World Quantum Day on April 11th 2025 with opening remarks from Senator Reed. Details are available here: https://physics.uri.edu/2025wqd/

 

 

Friday April 5. – noon-1 p.m.

Location: Bliss 190

Marco Alvarez, Associate Professor, Computer Science

“Transforming Research and Higher Education with Generative AI and Foundation Models”

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 amounts of unlabeled data, foundation models can serve as a powerful starting point for tackling a wide range of language and vision problems. By harnessing the power of these models, researchers can unlock new frontiers in interdisciplinary collaboration, innovation, and breakthrough discoveries. Moreover, this talk explores how generative AI is reshaping undergraduate and graduate education, presenting significant opportunities and challenges. Universities must quickly adapt to prepare students to work alongside intelligent machines and drive responsible AI innovation.

Friday March 29 – 2-3 p.m.

Location: Bliss 190

Sarah Brown, Assistant Professor, Computer Science

Title: How to anticipate risks in AI-enhanced research

ML-powered AI is increasingly appearing in all areas of research. However, ML (machine learning) is still in many ways a research output, more of a prototype than a polished, reliable, product. Even commercially released systems, because they are software, can be released without the same rigorous testing that commercially available research equipment has been traditionally be subject to. In this talk, I will draw connections between the ML research process and potential risks in applied ML and use of commercial AI in research.

AI Fireside Chat — Accelerating Materials Design and Discovery Using Machine Learning

Fireside Chat

Thursday February 29 12:30-1:30 – Conference Room A, Library (Large conference room on the second floor in the administrative area)

Accelerating Materials Design and Discovery Using Machine Learning 

 

Multiscale and Multiphysics Mechanics Lab 

Presenters: 

Sumanta Das, Associate Professor, Civil and Environmental Engineering || Mechanical, Industrial, and Systems Engineering

Bolaji Oladipo, Doctoral Candidate, Mechanical, Industrial, and Systems Engineering

Jonathan Villada, Doctoral Candidate, Civil and Environmental Engineering

Nausad Miyan, Doctoral Student, Civil and Environmental Engineering

Isaiah Narvaez, master’s student, Civil and Environmental Engineering

Abstract

Accelerating materials design and discovery is crucial to meet the rapidly evolving demands of technology and industry, enabling the development of smarter, more efficient, and sustainable solutions through novel engineering materials that display customizable and optimized properties. This interest is motivated either by specific application requirements or by a thoughtful scientific curiosity. Usually, the quest for these materials has been a laborious and costly endeavor, often relying on trial-and-error methods. It involves synthesizing and characterizing numerous compositions before arriving at the desired material. However, recent years have witnessed remarkable enhancements in this process through the adoption of high-throughput approaches. Leveraging advancements in artificial intelligence, particularly in machine learning adapted for materials science and engineering, data-driven materials prediction, and data-guided high-throughput characterization, has transformed the pace of materials design and discovery. These technological advancements now enable a significant acceleration in the identification and development of new materials.

Questions? Contact Drew Zhang, College of Business zhuzhang@uri.edu or Joan Peckham, Center for Computational Research – jpeckham@uri.edu

AI Fireside Chat on Bioinformatics — Feb 1st

Professor Drew Zhang from the College of Business and the CCR are working together to continue the Fireside Chats that we started last semester. The first one this semester will be as follows. I hope that you will join us. We promise short presentations with plenty of time for questions and discussion. 

Thursday February 1, 2024 – 12:30—1:30 Chris Hemme, Assistant Research Professor, Pharmacy

Large Conference Room in the Administrative Area of the Library on the Second Floor

Title:  The Changing Paradigm of Bioinformatics: Cloud Computing, Machine Learning, and Multiomics

Abstract:  The nature of biological data science is changing on a scale we haven’t seen since the sequencing of the Human Genome in the 1990’s.  With advances in experimental and computing technologies, we are now in the age of single-cell spatial multiomics analyses coupled to machine learning algorithms performed in cloud computing environments.  Experimentalists willing to embrace these technologies and data scientists willing to apply their skills to biological problems will be at a competitive advantage for jobs and research funding.

 

CCR Seminar: One Size Does Not Fit All: Towards AI for Everyone

Announcement — AI Distinguished Speaker Series
October 13, 2023: 9-11am, Library 3rd Floor Galanti Lounge

Dear URI community,

On 10/13 (9-11am, Galanti Lounge in the library) we’ll host the first lecture of the AI distinguished speaker series, delivered by Prof. Rada Mihalcea. Please see below for the abstract and her bio. More details will follow. If you are interested in meeting the speaker, please let me know, and I’ll coordinate the schedule.

One Size Does Not Fit All: Towards AI For Everyone 
Dr. Rada Mihalcea

Janice M. Jenkins Professor of Computer Science and Engineering University of Michigan, and Director of the Michigan Artificial Intelligence Lab

Recent years have witnessed remarkable advancements in AI, with language and vision models that have enabled progress in numerous applications and opened the door to the integration of AI in areas such as communication, transportation, healthcare, and arts. Yet, these models mainly adopt a one-size-fits-all strategy, failing to account for individual and group variations. In this talk, I will show some of the limitations and lack of representation of current AI models, and highlight the need for cross-cultural language and vision models that can capture the diversity of behaviors, beliefs, and language expressions across different groups. I will also explore ways in which we can address these limitations by developing models that are re-centered around people and their unique characteristics.

Rada Mihalcea is the Janice M. Jenkins Professor of Computer Science and Engineering at the University of Michigan and the Director of the Michigan Artificial Intelligence Lab. Her research interests are in computational linguistics, with a focus on lexical semantics, multilingual natural language processing, and computational social sciences. She serves or has served on the editorial boards of the Journals of Computational Linguistics, Language Resources and Evaluations, Natural Language Engineering, Journal of Artificial Intelligence Research, IEEE Transactions on Affective Computing, and Transactions of the Association for Computational Linguistics. She was a program co-chair for EMNLP 2009 and ACL 2011, and a general chair for NAACL 2015 and *SEM 2019. She is an ACM Fellow, an AAAI Fellow, and served as ACL President (2018-2022 Vice/Past). She is the recipient of a Sarah Goddard Power award (2019) for her contributions to diversity in science, an honorary citizen of her hometown of Cluj-Napoca, Romania (2013), and the recipient of a Presidential Early Career Award for Scientists and Engineers awarded by President Obama (2009).

Zhu (Drew) Zhang
Alfred J. Verrecchia Endowed Chair in Artificial Intelligence and Business Analytics
The University of Rhode Island
Kingston, RI 02881
401.874.4191
zhuzhang@uri.edu
https://web.uri.edu/business/meet/drew-zhang/

Research Computing Workshops — Fall 2023

1. Unity on-boarding workshop: Unity is our state-of-the-art HPC/AI platform with ~20,000 CPU-cores and ~1,300 GPUs built and operated in collaboration with the UMass system. Topics covered include logging on via SSH and Unity OnDemand, submitting batch and interactive command-line Slurm jobs for MPI parallelized or multithreaded applications, using the Unity OnDemand graphical applications (JupyterLab, RStudio, etc), and using the Unity OnDemand job composer. This workshop will be held remotely via Zoom. Please register for a Unity account prior to the workshop if you do not already have one at https://unity.uri.edu 

Date/Time: Monday, August 28, 2023 at 2 pm EDT
Zoom Link: https://umass-amherst.zoom.us/j/92991295046

2.  Data Carpentry workshop: Rachel Schwartz and Sarah Brown will be running their annual Data Carpentry workshop from August 31st — Sept 1st. The topic is “Introduction to Data Analysis in R” and is open to all researchers (recommended minimum advanced undergraduate) interested in learning how to use R in their work. Please register here. Thanks Rachel and Sarah! 

3. Singularity/Apptainer basics: ACCESS (formerly XSEDE) is holding a virtual workshop on “Containerizing HPC applications with Singularity/Apptainer” on Sept. 8th. Details appear here. 

4. Women in Bioinformatics: Two-day workshop that brings together researchers from computer science, data science, mathematics, statistics, biology, biotechnology, and chemistry to present their research in the interdisciplinary field of Bioinformatics. November 3-4 and Southern CT State University. Details appear here.

5. MGHPCC HPC Day: The MGHPCC HPC Day Conference will held at UMass Dartmouth this year on Nov 3rd. Website is up! 

6. URI Research Computing / AI Lab workshops: URI Research Computing and AI Lab teams will soon announce their coordinated plan for Fall semester workshops on R, Machine Learning (w/ Python), Tableau, Stats software and more. 

Research Computing Topical Workshops — Fall 2023

1. Unity on-boarding workshop: Unity is our state-of-the-art HPC/AI platform with ~20,000 CPU-cores and ~1,300 GPUs built and operated in collaboration with the UMass system. Topics covered include logging on via SSH and Unity OnDemand, submitting batch and interactive command-line Slurm jobs for MPI parallelized or multithreaded applications, using the Unity OnDemand graphical applications (JupyterLab, RStudio, etc), and using the Unity OnDemand job composer. This workshop will be held remotely via Zoom. Please register for a Unity account prior to the workshop if you do not already have one at https://unity.uri.edu

Date/Time: Monday, August 28, 2023 at 2 pm EDT
Zoom Link: https://umass-amherst.zoom.us/j/92991295046

2. Data Carpentry workshop: Rachel Schwartz and Sarah Brown will be running their annual Data Carpentry workshop from August 31st — Sept 1st. The topic is “Introduction to Data Analysis in R” and is open to all researchers (recommended minimum advanced undergraduate) interested in learning how to use R in their work. Please register here. Thanks Rachel and Sarah!

3. Singularity/Apptainer basics: ACCESS (formerly XSEDE) is holding a virtual workshop on “Containerizing HPC applications with Singularity/Apptainer” on Sept. 8th. Details appear here. 

4. Women in Bioinformatics: Two-day workshop that brings together researchers from computer science, data science, mathematics, statistics, biology, biotechnology, and chemistry to present their research in the interdisciplinary field of Bioinformatics. November 3-4 and Southern CT State University. Details appear here.

5. MGHPCC HPC Day: The MGHPCC HPC Day Conference will held at UMass Dartmouth this year on Nov 3rd.  Website is up! 

6. URI Research Computing / AI Lab workshops: URI Research Computing and AI Lab teams will soon announce their coordinated plan for Fall semester workshops on R, Machine Learning (w/ Python), Tableau, Stats software and more.