URI Department of Art and Art History presentsREVELATIONS! Education: Behind The Curtain – In Words and Images Exhibition September 15- December 11Join us for a reception and art conversations on September 17 4:30-7 in Lippitt Hall 401What is it like being in the classroom? What challenges do educator’s face? What restrictions are there preventing education from occurring? Are the requirements, expectations and limitations changing the way Educators function? What are the successes - giving students the opportunity to find their expressive voice?This exhibit will include the artwork and voices of a group of area educators from public, private and independent educational settings on all levels. The artist/educators will share their creative voices in various forms of expression as they also reflect on the challenges and celebrations they experience in and out of the classroom.Among the more than twenty artists are: Mary Jane Andreozzi, Deanna Camputaro, Ruth Clegg, Raff Diaz, Raphael Diaz, Tamara Diaz, Melanie Ducharme, Adam Ferbert, Susan Garland, Melissa Guillet, Catherine Davis-Hayes, Chris Kelley, Susan Kolenda, Jason Robert LaClair, Don Mays, Kerry Murphy, Sam Peck, Lisa Marie Ricci-Russo, Monique Rolle-Johnson, Jade Sisti, Janice Lee Strain, John Tedeschi, Katie Walck.On exhibit in URI Lippitt Hall (3rd & 4th floor), Green Hall and The Higgins Welcome CenterOpen To Monday – Friday 7:30-4:30, closed weekends and holidaysFor information: (401)286-4620/ spennell@uri.eduVisit URInvolved for complete event details.

Join us Monday, February 10th, from 1pm-3pm for an Advanced Track of Safe Zone! Today, we will be covering the topic of Race & Ethnicity at the Gender & Sexuality Center.Visit URInvolved for complete event details.

Join us Monday, February 10th, from 1pm-3pm for an Advanced Track of Safe Zone! Today, we will be covering the topic of Race & Ethnicity at the Gender & Sexuality Center.Visit URInvolved for complete event details.

See what it's like to be a PharmD student at URI College of Pharmacy.....attend a class with a current P3 PharmD student and take a tour of Avedisian Hall! Visiting students will have the opportunity to shadow a Pediatric/Geriatric Pharmacotherapy course with a currently enrolled student during the date/time noted. Note for admissions: Allow 3 students per block (Parents can attend prior to 2 pm class, but only students are permitted to shadow the class)

it-tls@uri.edu is inviting you to a scheduled Zoom meeting. Join Zoom Meeting https://uri-edu.zoom.us/j/97953147679?pwd=pIZ85cXEaY8Rww924MBwnVxJyJ0iBB.1 Meeting ID: 979 5314 7679 Passcode: 210183 --- One tap mobile +19292056099,,97953147679#,,,,*210183# US (New York) +13017158592,,97953147679#,,,,*210183# US (Washington DC) --- Join by SIP • 97953147679@zoomcrc.com Join instructions https://uri-edu.zoom.us/meetings/97953147679/invitations?signature=dZ-ykJszjI1jd0E5zORSY5dyeC2xqLbHwm814-oULNo

Please see below for the next talk in the fall seminar series organized by the Institute for AI & Computational Research on AI/ML techniques and applications across various scientific domains. You can find a table of upcoming talks here: https://web.uri.edu/iacr/seminars/.Speaker: Michael Puerrer (URI)Date/Time/Location: Oct 2, 3pm, Memorial Union room 308.Title: Simulation-Based Inference: Enabling Scientific Discoveries with Machine LearningAbstract: Modern science often relies on computer simulations to model complex systems — from the evolution of ice sheets and the spread of diseases to the merger of compact binaries. A central challenge is inference: learning about the hidden parameters of these systems from limited and noisy observations. Classical statistical methods rely on evaluating the likelihood function, but for realistic simulations the likelihood is often intractable or unavailable.Simulation-Based Inference (SBI) provides a powerful alternative. By leveraging simulations directly, and combining them with machine learning and Bayesian reasoning, SBI makes it possible to approximate posterior distributions without requiring a closed-form likelihood. I will introduce the core idea of SBI, outline the main methods — from approximate Bayesian computation to neural density estimation and deep generative models — and highlight how neural networks serve as surrogate models for inference.I will then survey applications across scientific domains, showing how SBI is enabling new discoveries in climate science, population genetics, and epidemiology, before turning to a detailed example in gravitational-wave astronomy, where SBI methods provide rapid, accurate parameter estimation for merging black holes and neutron stars.