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Microstructural Kinetics Group

Department of Materials Science & Metallurgy
 
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This is a superlist combining all those seminars on talks.cam taking place in one of the Departments of the School of Physical sciences, plus occasional other talks which would be of significant interest to researchers in the School. If you would like your talk or list included please contact Duncan (drs45)
Updated: 1 hour 25 min ago

Tue 09 Jun 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:43
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 02 Jun 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:42
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 26 May 16:30: Online Causal Inference Semina CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:42
Online Causal Inference Semina

CIF - Causal inference: From theory to practice and back again

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Tue 19 May 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:41
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 12 May 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:40
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 05 May 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:39
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 28 Apr 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:39
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 14 Apr 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:38
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 07 Apr 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:37
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 31 Mar 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:36
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 24 Mar 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:35
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 17 Mar 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:35
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 24 Feb 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:34
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 17 Feb 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:33
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 10 Feb 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:32
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 03 Feb 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:32
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Tue 13 Jan 16:30: Online Causal Inference Seminar CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:31
Online Causal Inference Seminar

CIF - Causal inference: From theory to practice and back again

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Mon 08 Dec 11:00: Boundaries and defects in high energy QCD: The Color Glass Condensate EFT BIDW03 - The Many Faces of Boundaries, Impurities, and Defects

Fri, 14/11/2025 - 15:31
Boundaries and defects in high energy QCD: The Color Glass Condensate EFT

QCD in Regge asymptotics can be formulated as a semi-classical boundary EFT of static color sources localized on a two-D surface and dynamical “wee” gauge fields in the bulk. We outline key features of this Color Glass Condensate EFT such as the classical solutions,  its connections to Reggeon Field Theory,  and powerful RG equations that describe the evolution of correlators of null WIlson lines in the EFT . 

BIDW03 - The Many Faces of Boundaries, Impurities, and Defects

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Tue 02 Dec 16:30: Online Causal Inference Seminar: Hunt and test for assessing the fit of semiparametric regression models CIF - Causal inference: From theory to practice and back again

Fri, 14/11/2025 - 15:30
Online Causal Inference Seminar: Hunt and test for assessing the fit of semiparametric regression models

We consider testing the goodness of fit of semiparametric regression models, such as generalised additive models, partially linear models, and quantile additive regression models: a class of problems that includes, for example, testing for heterogeneous treatment effects. We propose an approach that involves splitting the data in two parts. On one part, we “hunt” for any signal that may be present in the score-type residuals following a fit of the null model. On the remaining data, we test for the presence of the potential signal thus found.  In the first, hunting stage of the procedure, our framework allows the use of any flexible regression method chosen by the practitioner, such as a random forest. The method can therefore harness the power of modern machine learning tools to detect complex alternatives. A challenge in the testing step is that any first-order bias in the residuals may lead to rejection under the null. To address this, we employ a debiasing step, which we show is equivalent to performing a particular weighted least squares regression. We establish that the type I error can be controlled under relatively mild conditions and that the test has power against alternatives for which, with high probability, the hunted signal is correlated with the true signal in the score residuals.  

CIF - Causal inference: From theory to practice and back again

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