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X-WR-CALNAME:Uncertainty Quantification for Scientific Machine Learning
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UID:2025-01-13-dongxia-wu@cml.ics.uci.edu
DTSTAMP:20250113T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20250113T130000
DTEND;TZID=America/Los_Angeles:20250113T140000
SUMMARY:[CML Seminar] Dongxia Wu: Uncertainty Quantification for Scientific
Machine Learning
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Dongxia Wu\, PhD Student\, Dept. of Computer Science & Engineer
ing\, University of California\, San Diego\n\nTitle: Uncertainty Quantific
ation for Scientific Machine Learning\n\nAbstract: Scientific Machine Lear
ning (SML) is an emerging interdisciplinary field with wide-ranging applic
ations in domains such as public health\, climate science\, and drug disco
very. The primary goal of SML is to develop data-driven surrogate models t
hat can learn spatiotemporal dynamics or predict key system properties\, t
hereby accelerating time-intensive simulations and reducing the need for r
eal-world experiments. To make SML approaches truly reliable for domain ex
perts\, Uncertainty Quantification (UQ) plays a critical role in enabling
risk assessment and informed decision-making. In this presentation\, I wil
l first introduce our recent advancements in UQ for spatiotemporal and mul
ti-fidelity surrogate modeling with Bayesian deep learning\, focusing on a
pplications in accelerating computational epidemiology simulations. Follow
ing this\, I will demonstrate how quantified uncertainties can be leverage
d to design sample-efficient algorithms for adaptive experimental design\,
with a focus on Bayesian Active Learning and Bayesian Optimization.\n\nht
tps://cml.ics.uci.edu/seminars/2025-01-13-dongxia-wu
X-ALT-DESC;FMTTYPE=text/html:Dongxia Wu\, PhD Student\,
Dept. of Computer Science &\; Engineering\, University of California\,
San Diego
Title: Uncertainty Quantification for Scientific M
achine Learning
Abstract: Scientific Machine Learning (SML)
is an emerging interdisciplinary field with wide-ranging applications in d
omains such as public health\, climate science\, and drug discovery. The p
rimary goal of SML is to develop data-driven surrogate models that can lea
rn spatiotemporal dynamics or predict key system properties\, thereby acce
lerating time-intensive simulations and reducing the need for real-world e
xperiments. To make SML approaches truly reliable for domain experts\, Unc
ertainty Quantification (UQ) plays a critical role in enabling risk assess
ment and informed decision-making. In this presentation\, I will first int
roduce our recent advancements in UQ for spatiotemporal and multi-fidelity
surrogate modeling with Bayesian deep learning\, focusing on applications
in accelerating computational epidemiology simulations. Following this\,
I will demonstrate how quantified uncertainties can be leveraged to design
sample-efficient algorithms for adaptive experimental design\, with a foc
us on Bayesian Active Learning and Bayesian Optimization.
https://cml.ics.uc
i.edu/seminars/2025-01-13-dongxia-wu
URL:https://cml.ics.uci.edu/seminars/2025-01-13-dongxia-wu
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