See all upcoming seminars in LäsIT and seminar web pages at the homepage for the PhD studentseminars, TDB, Vi2, Theory and Applications Seminars (TAS) @ UpMARC., Department of Mathematics and The Stockholm Logic Seminar.
Disputation | PhD defense 9 June | Virginia Grande Castro: That's How We Role! A Framework for Role Modeling in Computing and Engineering Education Location: ÅNG 101195, Time: 13:15 Subtitle: A Focus on the Who, What, How, and Why Join via Zoom: Click here for Zoom link Opponent: Professor Alison Clear, Easter Institute of Technology Main Supervisor: Professor Mats Daniels, Uppsala university Abstract: Role model is a term used in everyday language and literature on education, particularly on diversity, equity, inclusion, and access, describing topics such as motivation and inspiration. However, role model, as a loosely defined concept, is understood and used in different ways. This shows the need for a shared vocabulary and structure to scaffold nuanced reflections and discussions on the who, what, how, and why of role modeling. This thesis describes the development of a framework for role modeling in computing and engineering education. It is focused on the role model’s perspective and is of particular use for educators as role models for students, although it can be used for others in this context. Educators were interviewed and surveyed, and the analysis comprised a phenomenographic approach, thematic coding analysis, argumentation, descriptive statistics, and group comparisons. The framework includes the dimensions of awareness and intention of role modeling. All educators are potential role models, regardless of whether we are aware of what we are role modeling and whether we intend for this to be emulated. What can be modeled is presented as achievements and aspects. As lenses to reflect on which ones a teacher should role model, we bring virtue ethics, care ethics, and ethics of freedom. Context and norms matter in role modeling, such as in who is a role model, as we argue for using research on identity and the history of computing. We provide examples of how and why educators role model (or not) care, emotions, and professional competencies outside norms in the disciplines. This thesis broadens how we understand and discuss role modeling in research and practice, including what can be modeled and obstacles to it. Practical examples (including reflection prompts) of how to use the framework are included for educators and other practitioners. |
Half-time seminar 12 June | Ivy Weber: High order Hermite Finite Element methods for wave equations Location: ÅNG 101142, Time: 10:15 Abstract:
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Licentiatseminarium | Licentiate seminar 12 June | Håkan Runvik: Modeling and Estimation of Impulsive Biomedical Systems Location: ÅNG 101130, Time: 13:00 External reviewer: Professor Torsten Wik, Chalmers
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Disputation | PhD defense 12 June | Elisabeth Wetzer: Representation Learning and Information Fusion, Applications in Biomedical Image Processing Location: Polhemsalen, ÅNG 10134, Time: 9:15 Opponent: Professor Fred Hamprecht, Heidelberg University
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Disputation | PhD defense 14 June | Natalia Calvo Barajas: Exploring Multidimensional Trust: Shaping Child-Robot Creative Collaborations in Education Location: ITC 10134, Time: 13:15 Opponent: Professor Kerstin Fischer, University of Southern Denmark
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Disputation | PhD defense 14 June | David Widmann: Reliable Uncertainty Quantification in Statistical Learning Location: Häggsalen (10132), Ångströmlaboratoriet, Time: 9:15
Mathematical models are powerful yet simplified abstractions used to study, explain, and predict the behavior of systems of interest. This thesis is concerned with their latter application as predictive models. Predictions of such models are often inherently uncertain, as exemplified in weather forecasting and experienced with epidemiological models during the COVID-19 pandemic. Missing information, such as incomplete atmospheric data, and the very nature of models as approximations ("all models are wrong") imply that predictions are at most approximately correct. Probabilistic models alleviate this issue by reporting not a single point prediction ("rain"/"no rain") but a probability distribution of all possible outcomes ("80% probability of rain"), representing the uncertainty of a prediction, with the intention to be able to mark predictions as more or less trustworthy. However, simply reporting a probabilistic prediction does not guarantee that the uncertainty estimates are reliable. Calibrated models ensure that the uncertainty expressed by the predictions is consistent with the prediction task and hence the predictions are neither under- nor overconfident. Calibration is important in particular in safety-critical applications such as medical diagnostics and autonomous driving where it is crucial to be able to distinguish between uncertain and trustworthy predictions. Mathematical models do not necessarily possess this property, and in particular complex machine learning models are susceptible to reporting overconfident predictions. The main contribution of this thesis are new statistical methods for analyzing the calibration of a model, consisting of calibration measures, their estimators, and statistical hypothesis tests based on them. These methods are presented in the five scientific papers in the second part of the thesis. In the first part the reader is introduced to probabilistic predictive models, the analysis of calibration, and positive definite kernels that form the basis of the proposed calibration measures. The contributed tools for calibration analysis cover in principle any predictive model and are applied specifically to classification models, with an arbitrary number of classes, models for regression problems, and models arising from Bayesian inference. This generality is motivated by the need for more detailed calibration analysis of increasingly complex models nowadays. To simplify the use of the statistical methods, a collection of software packages for calibration analysis written in the Julia programming language is made publicly available and supplemented with interfaces to the Python and R programming languages. |
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Internal seminars. Lecturers may be either internal or external.