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Title: Antarctic Meltwater alters Future Projections of Climate and Sea LevelSpeaker: Dr. Shaina Sadai, Five College AssociationAbstract: Imperfect understanding of ice sheet-climate interactions poses challenges for projecting the impacts of ice sheet mass loss on future climate and sea level. New research investigates ice sheet-climate dynamics by coupling a dynamic Antarctic ice sheet model and global climate model with offline coupling to a global sea level model. In our single-model, single-member modeling framework, we find sea level and climate projections are significantly modified from uncoupled simulations neglecting Antarctic meltwater under RCP8.5 and RCP4.5. Antarctic meltwater yields surface air temperatures up to 1.5°C higher in parts of the Northern Hemisphere, while broadly dampening temperature rise in the Southern Hemisphere. Due to radiative feedback changes, both emissions scenarios have global mean surface temperature warming ~0.3°C lower in the coupled scenario than the control by 2100, with a maximum anomaly of ~1°C at 2200 under RCP8.5. This slows Antarctica’s contribution to global mean sea level rise. Total Antarctic sea level contributions under RCP8.5 include substantial contributions from East Antarctica, though not under RCP4.5. We further find that regionally, projected sea level is up to 0.9 m higher in the Pacific than the global mean Antarctic contribution under RCP8.5 at 2200.Zoom link: https://uri-edu.zoom.us/j/96574497115?pwd=ASCzbx0hwb60wnX8S8VtsaOTQMnqbP.1

We are selling a variety of refreshing matcha lattes to support the Neuroscience Club! Stop by to grab a drink, learn more about our club, and fuel your brain with some delicious matcha. Every purchase helps fund our events, research activities, and community outreach.Visit URInvolved for complete event details.

As a result of his decades-long work as a filmmaker and activist, Byron is widely recognized as a leading voice in the fields of independent filmmaking and gender violence prevention. He serves as an adjunct professor at Columbia University, and was a filmmaker-in-residence at American University.Byron’s gender violence prevention lectures focus on how hyper-masculinity in popular culture normalizes male violence; how commonalities between race, class, and gender link oppression; how homophobia and transphobia make LGBT communities vulnerable to male violence; how positive male leadership and bystander intervention can end gender-based violence; and how to use cis male privilege to ally with women and girls to redefine masculinity and promote healthy relationships.Visit URInvolved for complete event details.

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/. Note the special time and date for this seminar.Speaker: Ritambhara Singh (Brown) Date/Time/Location: Fri, Oct. 17, noon, Memorial Union room 308. Title: The promise and pitfalls of integrative AI in healthAbstract: Artificial intelligence (AI) is increasingly being integrated into health and medicine, with the promise of revolutionizing how we diagnose disease, discover new therapies, and personalize care. Integrative AI methods—approaches that combine data from multiple sources, such as graphs, medical images, electronic health records, and genomic profiles—offer a unique opportunity to capture the complexity of human health in ways that no single data type can achieve. For example, we have shown that AI methods that merge domain-specific knowledge graphs with generalized reasoning approaches can support out-of-the-box drug-based predictions. Similarly, integrating diverse types of medical data—such as genomic information, clinical records, and imaging—can enable more accurate and personalized predictions than any single data source alone.Yet the path forward is far from straightforward. Not every integration is beneficial, and in some cases, combining information can add noise or introduce hidden biases. Our recent experience with vision–language models (VLMs), which integrate text and images, highlights these concerns regarding correctness, consistency, and trustworthiness. These challenges remind us that integrative AI is not simply about adding more data, but about ensuring that the integration is meaningful and reliable.In this talk, I will explore both the promise and pitfalls of integrative AI in health. We will examine how integration can accelerate discovery, while also exploring the questions that arise for AI methods being applied in medicine. By weighing both opportunities and limitations, this talk invites us to think critically about how to advance integrative AI responsibly in the health domain.