Nettsider med emneord «data science»
João Gama, Associate Professor at the University of Porto, talks about the limitations of current machine learning and data mining algorithms in dealing with real-time data.
Can we make people more aware of energy consumption? In other countries, people are aware of their energy use because energy is expensive or only accessible a few hours per day. How to do this in Norway where relatively cheap energy is available 24/7? In this master project, a team of students will work towards an installation in which energy consumption is visualised – and maybe even heard or felt – in a creative and informative manner.
The PLUMBIN' project is a cross-disciplinary collaboration between high-energy physics and statistics aimed at making powerful computational tools for exploring the fundamental constituents of the Universe.
The combination of green growth and digitalisation is presented as the transition needed toward a more sustainable future (EU Science Hub, 2022).
The Sustainability Lab at IFI owns a so-called Precious Plastic machine, which includes a shredder, an injector, and an extruder. The machine is used to recycle plastic materials, such as left-overs from IFI’s 3D-printers, and to form them into new plastic artefacts. The machine can be seen in the Sustainability Lab and during the upcoming Forskningsdagene September 23-24, 2023 at Climate House in the Botanical Gardens.
Bayesian methods have recently regained a significant amount of attention in the machine community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using Bayesian approach: Parameter and prediction uncertainty become easily available, facilitating rigid statistical analysis. Furthermore, prior knowledge can be incorporated.
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On the 31st of October, all BigInsight partners met at the offices of Skatteetaten to share recent developments and plans for the road ahead.
The transition to a globally sustainable low-carbon emission society requires a significant increase in the use of renewable energy. We are facing increased decentralized energy production, and digitalization of the whole value chain. Advanced use of ICT is crucial for realizing this energy shift. This is confirmed in the revised national strategy for Norway on energy research, Energy21 which recommends to give "Digitalization and integrated energy systems" top priority. In this context, the energy sector is facing two key challenges: 1) the sector must be able to apply state-of-the-art ICT, 2) there is a lack of talents with the necessary expertise in the intersection between energy systems and ICT, which we refer to as energy informatics. This project, which is a collaboration between research groups at University of Stavanger, University of Oslo, Technical University of Munich, and University of Lille, is one response to these challenges.
The project "Pandemics, Particles and Parameter (s)paces" is a cross-disciplinary collaboration between theoretical physics at the University of Oslo and the Norwegian Institute of Public Health (NIPH/FHI) aimed at developing software to meet the United Nations Sustainable Development Goal 3, in particular Target 3.d: "Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks."
Modern science usually provides both copious amounts of data and complicated models for the part of reality it is trying to describe. Often there is even so much data, and the models so complicated, that it becomes difficult to make full use of the data in deciding which models best describe the world around us, and finding their properties. The main goal of the GAMBIT project is to develop a software tool to help physicists do just that.
The SANT project develops resources for Sentiment Analysis for Norwegian Text. While coordinated by the Language Technology Group (LTG) at IFI/UiO, collaborating partners include NRK, Schibsted and Aller Media.
Seismic arrays are installations where several seismic sensors are typically distributed over an aperture covering up to 10 km or more. Routine array signal processing is applied to the data streams recorded. This generates near-realtime estimates of direction-of-arrival for impinging signals that, for example, are due to earthquakes.
Globally, seismic data from arrays have proven to be of great value, for example, in characterization of earthquakes, in monitoring that the nuclear test-ban-treaties to limit nuclear testing, in vulcanology, in glaciology, in monitoring CO2 storage sites, and beyond.
Squarehead Technology is a world leader in precision acoustic detection. Their technology utilizes an array of microphones and beamforming to provide spatial filtering and awareness. Loosely speaking, we can provide a superhearing ability by pinpointing the direction of a given sound source, and classify the type of sound we’re listening to.
The types of MSc theses that I could offer fall within three categories:
- Project specific: the focus is here is in management/analytics of domain specific data (e.g. company information), this means the thesis will focus on specific datasets and will try to obtain insights in various forms from the datasets
- DataGraft related: the focus here will be to develop/validate generic technology, mostly related to big data scalable solutions for working with large datasets; tasks will be more software design/engineering in nature
- Data exchange related: the focus here will be to develop/validate technology specifically related to data exchange; tasks will be technologically exploratory, involve some design/engineering work
Successful data science requires a mixture of statistics, computer science and domain knowledge. In DataScience@UiO, we bridge the first of these gaps by bringing statisticians and computer scientists from the Factulty of Mathematics and Natural Sciences together, and the other by bringing in industry-heavy research centers such as BigInsight and SIRIUS. In this blog-post, I want to say something about why I believe SIRIUS has a very important role to play in the current and future data science engagement at the University of Oslo.