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DTSTART:20070311T020000
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DTSTAMP:20260920T052133
DTSTART;TZID=America/Detroit:20260922T113000
DTEND;TZID=America/Detroit:20260922T130000
SUMMARY:Workshop / Seminar:2026 National Postdoc Appreciation Week
DESCRIPTION:National Postdoc Appreciation Week (NPAW) 2026 is an annual event sponsored by the National Postdoctoral Association. The week honors the research\, innovation\, and vital contributions of postdoctoral fellows. This year’s theme is celebrating 150 years of the U.S. postdoc position. 
UID:149939-21907401@events.umich.edu
URL:https://events.umich.edu/event/149939
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Sessions
LOCATION:Assembly Hall, 4th floor
CONTACT:
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DTSTAMP:20260910T075005
DTSTART;TZID=America/Detroit:20260922T113000
DTEND;TZID=America/Detroit:20260922T130000
SUMMARY:Workshop / Seminar:Black-box learners for white-box epidemic models
DESCRIPTION:Mechanistic epidemic models are attractively interpretable\, but fitting realistic models to imperfect data often involves challenging inverse problems. This talk presents two recent projects that explore hybrid approaches that combine deep learning with traditional mechanistic transmission models. First\, I will discuss recent attempts of using neural posterior estimation (NPE) for simulation-based inference of stochastic epidemic models. When partial observation and large latent state spaces make likelihood-based inference computationally difficult\, NPE learns to invert a stochastic simulator and approximate the posterior distribution of model parameters in a likelihood-free manner. Second\, I will present a spatiotemporal epidemic-informed neural network approach based on physics-informed neural networks (PINNs). This approach incorporates a spatial interaction matrix to account for spatial or geographical dependencies in modeling epidemics across multiple regions (e.g.\, counties\, states\, etc.). The interaction matrix can be informed by external data or auxiliary information such as cross-region distance\, adjacency\, or mobility. These two projects provide practical examples of how flexible “black-box” machine learners can support inference for structured\, interpretable “white-box” epidemic models.
UID:151884-21912331@events.umich.edu
URL:https://events.umich.edu/event/151884
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Epidemiology,Complex Systems,Disease,Epidemics,Stochastic Dynamics,Machine Learning
LOCATION:Weiser Hall - 747
CONTACT:
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