Research Director Anette Stephansen says this recognition is at the very highest level within applied mathematics:
– Receiving recognition from the Society for Industrial and Applied Mathematics (SIAM) is among the highest distinctions in applied mathematics. Publishing in SIAM is in itself a major achievement. That Xue-Cheng Tai has now been named a SIAM Fellow for his research places him at the very top in terms of the honor one can receive within applied mathematics. We are very proud of this, says Stephansen.
Tai is being honored for his many research contributions to convergence analysis of numerical models, the development of numerical methods, and the use of fast numerical algorithms in image processing—especially methods based on partial differential equations.
Among other things, he has developed efficient algorithms such as the Additive Operator Splitting (AOS) method, Piecewise Constant Level Set Methods (PCLSM), Fast nonlinear multigrid and other fast numerical algorithms that break down complex equations into steps computers can solve quickly and accurately. These tools are now widely used in image processing, engineering, and computer science.
More accurate weather forecasts, safer engineering design, and better medical treatment
Tai has worked at NORCE for 3 years and is part of the Digital Systems group.
In what way is your research groundbreaking compared with how things were done before?
In the past there was always a trade-off: you could get fast or accurate results, but rarely both. For example, it could take a long time to obtain a sharp medical image - or you could get a quick result that was not reliable. My work, together with many collaborators, has helped remove this compromise. By developing algorithms that are both fast and mathematically proven to be accurate, we can now achieve high-quality results in real time.
For example, in fluid dynamics our algorithms make it possible to simulate complex flows—such as ocean currents, weather systems, or blood flow in blood vessels - with far higher speed and stability than before. This can enable more accurate weather forecasts, safer engineering design, and better medical treatment.
In artificial intelligence, our mathematical analysis has helped explain and improve the performance of advanced AI models such as transformers and neural networks. This makes AI systems not only more powerful, but also more transparent and trustworthy—something that is crucial for use in sensitive areas such as health and autonomous systems.
These advances have inspired many new developments in computational mathematics, image analysis, and data-driven science.
Relevant to everyday technology, medical diagnostics, and artificial intelligence
What has become easier?
Trust and automation. When these algorithms are underpinned by solid mathematical analysis, engineers, researchers, and doctors can have greater confidence in the results—even in critical situations. It is now much easier to automate tasks such as “cleaning” noisy data or identifying difficult objects in demanding scenarios, without manual tuning in every single case. This reliability is crucial for applications such as medical imaging, industrial inspection, and scientific computing.
Where is this knowledge important?
This research impacts many parts of our everyday lives and technology:
- Everyday technology: Many apps on phones and computers - for example for image enhancement, photo editing, and even video streaming - depend on fast and reliable algorithms developed in this field. These advances make our digital experiences smoother and more efficient.
- Medical diagnostics: The methods are used to reconstruct and enhance MRI and CT images, and to help AI identify patterns in medical images for early disease detection. For example, this can give doctors clearer images and greater confidence in their decisions.
- Artificial intelligence: My recent work helps explain why new AI models such as transformers and neural networks work so well. By making these systems more transparent and reliable, we can build AI that is not only powerful, but also easier to understand and trust. This is important for AI’s future role in society.
- Engineering and physics: These algorithms are central to simulations for reservoir modeling, maritime safety, and many other engineering applications where fast and precise calculations are critical.
– In short, the impact of my research spans everything from the everyday technology we use daily to the most advanced tools in science, medicine, and industry.
Tai emphasizes that many people have contributed to developments in these areas.
– My contributions are only one part of a large and vibrant research community, he concludes.
Official ceremony in the United States in July
Xue-Cheng Tai has received many congratulations since the SIAM Fellow recognition became known shortly before Easter.
– It has been nice to receive so many different congratulations, and it has been especially gratifying to be contacted by several renowned researchers who have noticed the recognition I have received, says Tai.
In July, Xue-Cheng Tai will receive the SIAM Fellow recognition in Philadelphia, USA.
– Being recognized alongside the world's leading scientists in my field feels truly significant. I am honestly surprised by the selection, knowing how many exceptional researchers contribute to this discipline. I am very happy and honored to receive this fellowship," concludes Xue-Cheng Tai.
