Center for Data Science

News archive for Center for Data Science

On 23 April 2022, The Faculty of Mathematics and Natural Sciences, in cooperation with several of the University of Bergen's partners, organized OPPLEV på Marineholmen, a knowledge festival aimed at both young and older participants, with or without a science background. The Center for Data Science (CEDAS), in cooperation with the Statistics and Data Science group, presented a «Probability Wheel... Read more
On 1 April 2022, the Statistics & Data Science group and the Center for Data Science (CEDAS) held a cross-faculty workshop on Cooperation Opportunities and Challenges in Data Science Education. The main aim of the workshop was to enhance the interaction between the departments and faculties involved in CEDAS, and start a discussion about how to improve UiB’s educational offer in data science.
This seminar will present some of the speakers' space-level AI research activities to assess storm-caused damage to infrastructure networks.
Setting up data science institutions poses rather fundamental questions: What kind of institutions are we building? Who are we serving? What are the politics? In this talk I would like take stock of the larger context: a world lacking futures, fragmented academic disciplines, and the dystopian use of technology.
The talk will be focusing mainly on spatio-temporal data such as molecular dynamics simulations which are unfavorably known to be hard to visualize. In addition, the speaker will touch upon other biochemical datasets where the “curse of dimensionality” poses the biggest visualization challenge.
The very first CEDAS-conference (Center for Data Science at the University of Bergen) was held the 1st and 2nd of June both virtually and physically in Bergen.
Does your work involve data science? Are you curious about research in data science in Bergen? The CEDAS conference 2021 will be a 2-day event featuring talks and discussion by leading international scientists and local experts on the interaction between data science, statistics, machine learning, and AI, as well as their applications in science and society.
På Universitet i Bergen (UiB) vokser miljøet innen maskinlæring stadig. Kanskje ikke så rart, når kunstig intelligens er i ferd med å endre hele samfunnet vårt.
What I currently think about machine learning in health, is that it will lead to better health in the world I will try to touch several issues, using examples from the literature and mine (this will be mainly breast cancer models for personalized therapy), among which bias, intelligence (the dynamic one and the hard-coded one), explain black box models (or stop using them?) and slow-AI.
Iain George Johnston will talk about HyperTraPS (hypercubic transition path sampling), a highly generalisable Bayesian approach which efficiently learns evolutionary and progression pathways from cross-sectional, longitudinal, or phylogenetically linked data.
The speaker will focus on the following topics: inferring topological invariants of latent structures from data, enriching dimension reduction layouts, and modeling human perception to design more trustworthy Machine Learning techniques.