SAIL – Summit for AI Institutes Leadership
Announcing the upcoming SAIL '23 Leadership Summit in Atlanta, Georgia. Register here: https://na.eventscloud.com/website/57313/
10 events found.
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IESP webinar
Zoom webinarTo help clarify the vision of the AIVO expansion program International Engagement Support Program and help Institutes identify how this funding support might advance their work, Ryan Burns is hosting a webinar next month where he will give a brief overview of the program and then answer your questions. The Zoom link is below. HopeContinue reading "IESP webinar"
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Featured
Workshop: Common Task Framework for Science and Engineering
Machine learning and AI are used broadly in science and engineering, with a tremendous diversity of architectures and algorithms being developed across disciplines and applications. The AI Institute in Dynamic Systems aims to build a common task framework (CTF) for evaluating algorithms with an overarching goal to develop a taxonomy of enabling architectures and algorithmsContinue reading "Workshop: Common Task Framework for Science and Engineering"
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AgAID Seminar Series: Strategic Science Communication & Artificial Intelligence
Join AgAID for a three-part seminar series delving into public perception and engagement with evolving AI technology. In this seminar series, Science Communication Specialist, Dr. Alex Kirkpatrick, will address challenges, opportunities, research, and best practices in communicating about AI. See below for event times and Zoom links. 03/01/2024, 9-10 a.m. PST: On the Public UnderstandingContinue reading "AgAID Seminar Series: Strategic Science Communication & Artificial Intelligence"
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TILOS webinar on AI Ethics in Research – Friday 3/8 at 12:00 p.m. PST
Zoom webinarTune into The Institute for Learning-enabled Optimization at Scale (TILOS)'s webinar on AI Ethics in Research. Attendees will learn frameworks for integrating ethics into AI development, strategies for measuring bias and fairness, and gain insights into how optimization can reveal and mitigate societal biases in AI algorithms. Follow the link below for more information: https://tilos.ai/events/ai-ethics-in-research-webinar/
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AAAI 2024 Spring Symposium Series
Stanford UniversityAAAI 2024 Spring Symposium Series will be hosted at Stanford University on March 25-27, 2024 as part of the Association for the Advancement of Artificial Intelligence (AAAI) conference. The conference will include the following eight symposia: Bi-directionality in Human-AI Collaborative Systems Clinical Foundation Models Symposium Empowering Machine Learning and Large Language Models with Domain andContinue reading "AAAI 2024 Spring Symposium Series"
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ICICLE & OSU CSE Distinguished Seminar Series
We invite you to join us for a joint ICICLE & OSU CSE Distinguished Seminar Series on Thursday, May 9 at Noon, EST featuring Prof. B.S Manjunath from the University of California, Santa Barbara. He will present his work on Bisque, a robust, open-source cloud-based platform designed for reproducible computer vision. https://icicle.osu.edu/media/document/2024-04-24/icicle-seminar-manjunath_05092024-1.pdf
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Dynamics AI: Data-Driven Science & Engineering Seminar Series
University of WashingtonJoin us for our ongoing Data-Driven Science & Engineering Seminar Series organized by the AI Institute in Dynamic Systems. Visit our website to sign up for the seminar mailing list to receive future announcements and to view recordings from past events!
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ICICLE Seminar Series featuring Professor Laura Schmidt and Dr. Amarnath Gupta
Zoom webinarThe ICICLE Seminar Series is happy to announce the next set of speakers for June 13th, 2024: Professor Laura Schmidt from UCSF and Dr. Amarnath Gupta from the San Diego Supercomputer Center will be joining us to talk about NSF Convergence Accelerator Track J Award entitled NOURISH, the Network Of User-Engaged Researchers building Interdisciplinary Scientific Continue reading "ICICLE Seminar Series featuring Professor Laura Schmidt and Dr. Amarnath Gupta"
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Workshop | Machine Learning for Science & Engineering
University of WashingtonPhysics-informed Machine Learning (ML) continues to emerge as a leading paradigm integrating artificial intelligence (AI) with engineering dynamic systems. This approach provides new capabilities in real-time sensing, learning, decision-making, and predictions that are ethical, efficient, reliable, safe, and imbued with uncertainty quantification. The foundations of physics-informed ML are rooted in four key disciplines: (i) controlContinue reading "Workshop | Machine Learning for Science & Engineering"