The University of Oslo is partially closed. The PhD defence and trial lecture will therefore be digital and streamed directly using Zoom. The host of the session will moderate the technicalities while the chair of the defence will moderate the disputation.
Ex auditorio questions: the chair of the defence will invite the audience to ask questions ex auditorio at the end of the defence. If you would like to ask a question, click 'Raise hand' and wait to be unmuted.
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Join the disputation
The webinar opens for participation just before the disputation starts, participants who join early will be put in a waiting room.-
Submit the request to get access to the thesis (available from 13th August 13:15, until 27th August 13:15)
Trial lecture
"Gaussian process covariance parameter estimation: from theoretical results to practical challenges"
Main research findings
Artificial Intelligence (AI) and data-driven decisions based on Machine Learning (ML) are making an impact on an increasing number of industries. As these autonomous and self-learning systems become more and more responsible for decisions that may ultimately affect the safety of people, assets, or the environment, ensuring the safe use of AI will be crucial.
This thesis aims to provide some of the tools needed to make data-driven modeling suitable for use in safety-critical systems, like a ship, offshore structure, or a spacecraft. This is challenging when we are faced with complex physical phenomena, in environments with a high degree of uncertainty, and where the consequence of an erroneous decision can be catastrophic. To succeed, the knowledge we possess about these phenomena must be exploited optimally.
We consider various ways in which knowledge about the underlying physical system can be incorporated into probabilistic models. This includes how to make use of expensive computer simulations most efficiently, and how physics-based knowledge can be used as constraints to obtain “physically obedient machine learning models”. With this approach, we develop algorithms that can be used to search for optimal decisions in uncertain and safety-critical environments.