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<span style="font-size: 14pt;"><b><i>"Generative Machine Learning Models for Uncertainty Quantification"</i></b></span>
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<div class="ContentPasted0"><span style="font-size: 14pt;"><b>Feng Bao</b></span></div>
<div class="ContentPasted0">Timothy Gannon Endowed Associate Professor of Mathematics</div>
<div class="ContentPasted0">Department of Mathematics, Florida State University</div>
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<div class="ContentPasted0">Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic.
<b>All are welcome to attend. </b></div>
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<div class="ContentPasted0">NOTE: In-person attendance is requested. Zoom access is intended for external (non-departmental) participants only.
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<div class="ContentPasted0"><b><a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Ffsu.zoom.us%2Fj%2F94273595552&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7C640f7ed3de7e426a983508dc4f4c2349%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638472435618689648%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=SsPVpVs8EVVaRaJ6YvfrXygSsfcI5NqGYZ0KK%2Fo2CoI%3D&reserved=0" originalsrc="https://fsu.zoom.us/j/94273595552" shash="snAaYq1+MQgCnRWvm9m8UeeFI690NRibqCyTPmgrQO1TQVTur09LkExTZxz1Q1Aw7svn5VKF19JLQ21tZ9GkJTuRxMHhLQo1AocbxiaDu2GuwKGJg+oQRURq47cy9Q9kVN30V+LJHmhBAazKQYESBXr86JwQq/mUKCaMn/KaWYk=">https://fsu.zoom.us/j/94273595552</a> </b></div>
<div>Meeting # <b>942 7359 5552<br>
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<div class="ContentPasted0">🎦 Colloquium recordings will be made available here,
<a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fcolloquium&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7C640f7ed3de7e426a983508dc4f4c2349%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638472435618845910%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=Ha4a0IzQSBg2CzVyIMXi%2BDZe7LQB61iKRsU%2BOP7G%2FWk%3D&reserved=0" originalsrc="https://www.sc.fsu.edu/colloquium" shash="eukENBTS3h8QrktM2qOenZ6VwyHjMrIkd4yphiOKpWHna6jNrRFZmAugYp7dWxotZhbui0LpSDUqFCQOGBfSvJ9m/OH+4X0z8D2Vtmumvt1xJbXtsfUd99SeVtodq0zw28IpG5PQ/zEV7okLJ00nW7mUUDhdQ8JY99BgHP9B4cU=" id="OWAe38c7a4c-07fa-a22e-87fe-a1445539a69f" class="OWAAutoLink">
sc.fsu.edu/colloquium</a> </div>
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<div><b style="font-family: inherit; font-size: inherit; font-style: inherit; font-variant-ligatures: inherit; font-variant-caps: inherit;">Wednesday, Apr 3, 2024, Schedule: </b><br>
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<div class="ContentPasted0">* 3:00 to 3:30 PM Eastern Time (US and Canada) </div>
<div class="ContentPasted0">☕ Nespresso & Teatime - 417 DSL Commons </div>
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<div class="ContentPasted0"><b>* 3:30 to 4:30 PM Eastern Time (US and Canada) </b>
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<div class="ContentPasted0"><b>🕟 Colloquium - 499 DSL Seminar Room </b></div>
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<div class="ContentPasted0"><b>Abstract: </b></div>
<div class="ContentPasted0">Generative machine learning models, including variational auto-encoders (VAE), normalizing flows (NF), generative adversarial networks (GANs), diffusion models, have dramatically improved the quality and realism of generated content,
whether it's images, text, or audio. In science and engineering, generative models can be used as powerful tools for probability density estimation or high-dimensional sampling that critical capabilities in uncertainty quantification (UQ), e.g., Bayesian inference
for parameter estimation. Studies on generative models for image/audio synthesis focus on improving the quality of individual sample, which often make the generative models complicated and difficult to train. On the other hand, UQ tasks usually focus on accurate
approximation of statistics of interest without worrying about the quality of any individual sample, so direct application of existing generative models to UQ tasks may lead to inaccurate approximation or unstable training process. To alleviate those challenges,
we developed several new generative diffusion models for various UQ tasks, including diffusion-model-assisted supervised learning of generative models, a score-based nonlinear filter for recursive Bayesian inference, and a training-free ensemble score filter
for tracking high dimensional stochastic dynamical systems. We will demonstrate the effectiveness of those methods in various UQ tasks including density estimation, learning stochastic dynamical systems, and data assimilation problems.</div>
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<div class="ContentPasted0">Additional colloquium details can be found here,</div>
<div class="ContentPasted0"><a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fnews-and-events%2Fcolloquium%2F1778-colloquium-with-feng-bao-2024-04-03&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7C640f7ed3de7e426a983508dc4f4c2349%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638472435618845910%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=O%2FJNy878F%2BXQ8e90slANreJQiXP64ZfgR%2FmzveX3thU%3D&reserved=0" originalsrc="https://www.sc.fsu.edu/news-and-events/colloquium/1778-colloquium-with-feng-bao-2024-04-03" shash="u61Ce5MUA60gL/IGJnFEwECXQ5eGKzRF8Y8cAcpWeVDUjiAASWu0WGbMwbJ3VOA3E2jsmV4HxsmiqfG0Bc2A3UJsUbDs7A94NJACy4dR9wtTRHmQ1hN9m8ta7FwvX6M0MyXH+AVAgl2vrBjyXoPWvwcOmrTBCjaU4kWtqQaSyRU=" style="font-family: Tahoma, Geneva, sans-serif; font-size: 12pt;" id="OWA92e14c3d-6c33-031a-3754-5c759fc8a822" class="OWAAutoLink">sc.fsu.edu/news-and-events/colloquium/1778-colloquium-with-feng-bao-2024-04-03</a></div>
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