Opening Keynote, 7 August 2023
Speaker: Arvid Lundervold, University of Bergen, Norway
Title: AI and predictive modelling y ≈ f(X,θ) in medicine and biology
Abstract: This talk aims to inspire and motivate participants, offering a blend of neuroscience, AI, and predictive modelling, demonstrating the power of interdisciplinary thinking in biomedicine. We will discuss the exciting intersection of AI and predictive modelling, and its transformative potential in medicine and biology. We will make use of the predictive modelling framework “y ≈ f(X,θ)” and also draw parallels between the nature of generative AI and biological processes, including how the brain seems to implement language, learning, and memory.
We’ll look at some real-world examples, showcasing how these technologies can revolutionize healthcare and biological research. From predicting disease progression to personalizing treatments, these tools are becoming integral in our quest to understand and improve life and health.
The talk will also address ethical and technological challenges, emphasizing the importance of data privacy, algorithmic transparency, and infrastructure. We will conclude with a forward-looking discussion, highlighting emerging trends and the potential role of early-stage researchers in shaping this exciting field.
Invited talk Statistics, 8 August 2023
Speaker: Mattias Villani, Stockholm University, Sweden
Title: An Introduction to Bayesian Learning for Uncertainty Quantification and Decision Making
Abstract: Data science and machine learning is inherently about making decisions under uncertainty. In this lecture I will explain how the Bayesian approach gives a natural quantification of uncertainty, in a form that is directly useful for decision making. I will also briefly discuss how computer simulation is used as an everyday tool for Bayesian computations, and introduce a couple of probabilistic programming languages that can be used to perform a Bayesian analysis with minimal effort.
Invited talk Machine Learning, 9 August 2023
Speaker: Vaneeda Allken, Institute of Marine Research, Norway
Title: Bridging the gap between theory and practice AI
Abstract: Although machine learning models are getting increasingly sophisticated and becoming more widely accessible to the general public, many enterprises still struggle to put these methods to practical use. One prominent cause of this is that the impressive performance obtained on benchmark data often fails to translate to real-world use cases. It is challenging to build sufficiently representative datasets from scratch. Developing an effective model also requires a clear understanding of the objective and the manner in which the model will be deployed. Proper communication with the domain experts and the users is important. In this talk, I will discuss some of the roadblocks we faced while developing and implementing machine learning models in a practical setting, possible solutions and suggestions for the way forward.
Invited talk Visual Data Science, 10 August 2023
Speaker: Jan Byška, Masaryk University, Czech Republic, and University of Bergen, Norway
Title: Visualizing Large Biomolecular Data
Abstract: With recent technological advancements, we can simulate or gather extensive datasets through experimentation. While this available data opens doors to unprecedented discoveries, it also presents notable hurdles in subsequent analysis. In this invited talk, I will discuss the difficulties of working with extensive biomolecular datasets, and I will provide instances illustrating how the field of visual data science can effectively address these issues. The presentation will primarily center around extensive spatio-temporal data such as molecular simulations consisting of millions of timesteps or larger drug design datasets for virtual screening.