19/1, Anna Dreber Albenberg, Stockholm School of Economics: (Predicting) replication outcome
Abstract: Why are there so many false positive results in the published scientific literature? And what is the actual share of results that do not replicate in different literatures in the experimental social sciences? I will discuss several large replication projects on direct and conceptual replications, as well as our studies on "wisdom-of-crowds" mechanisms like prediction markets and forecasting surveys where researchers attempt to predict replication outcomes as well as new outcomes.
2/2, Claudia Redenbach, Technische Universität Kaiserslautern: Using stochastic models for segmentation and characterization of spatial microstructures
Abstract: The performance of engineering materials such as foams, fibre composites or concrete is heavily influenced by the microstructure geometry. Quantitative analysis of 3D images, provided for instance by micro computed tomography (µCT), allows for a characterization of material samples. In this talk, we will illustrate how models from stochastic geometry may support the segmentation of image data and the statistical analysis of the microstructures. Our first example deals with the estimation of the fibre length distribution from µCT images of glass fibre reinforced composites. As examples of segmentation tasks we present the reconstruction of the solid component of a porous medium from focused ion beam scanning electron microscopy (FIB-SEM) image data and the segmentation of cracks in µCT images of concrete.
16/2, Fredrik Johansson, Chalmers: Making the most of observational data in causal estimation with machine learning
Abstract: Decision making is central to all aspects of society, private and public. Consequently, using data and statistics to improve decision-making has a rich history, perhaps best exemplified by the randomized experiment. In practice, however, experiments carry significant risk. For example, making an online recommendation system worse could result in millions of lost profits; selecting an inappropriate treatment for a patient could have devastating consequences. Luckily, organizations like hospitals and companies who serve recommendations routinely collect vast troves of observational data on decisions and outcomes. In this talk, I discuss how to make the best use of such data to improve policy, starting with an example of what can go wrong if we’re not careful. Then, I present two pieces of research on how to avoid such perils if we are willing to say more about less.
2/3, Andrea De Gaetano, IRIB CNR: Modelling haemorrhagic shock and statistical challenges for parameter estimation
Abstract: In the ongoing development of ways to mitigate the consequences of penetrating trauma in humans, particularly in the area of civil defense and military operations, possible strategies aimed at identifying the victim's physiological state and its likely evolution depend on mechanistic, quantitative understanding of the compensation mechanisms at play. In this presentation, time-honored and recent mathematical models of the dynamical response to hemorrhage are briefly discussed and their applicability to real-life situations is examined. Conclusions are drawn as to the necessary formalization of this problem, which however poses methodological challenges for parameter estimation.