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RICAM Special Semester on Mathematical Methods in Medicine

Activity: Participating in or organising an eventOrganising a conference, workshop, ...

Description

In the last two decades, the predictive nature of mathematical and computational models has been enhancing the understanding of numerous physiopathological dynamics and the design of therapeutic devices. In silico models are today a regular support not only for the investigative activity of medical doctors and life scientists, but also for advanced clinical practice and the development of healthcare strategies. Still, problems from biomedical research are extremely complex and challenging from the modeling viewpoint. Typically they are characterised by remarkable heterogeneities and multi-scale dynamics, and are imbued with uncertainty. Finally, the huge amount of data currently available requires efficient algorithms that can continuously learn from the generated data, be they clinical or virtual.
In such a framework, the RICAM Special Semester on "Mathematical Methods in Medicine" gathers experts from the modeling, clinical and biological sides to foster the interaction between the two communities, with also the aim to identify relevant challenges for the upcoming years. The semester will account for thematic workshops focusing on cardiovascular diseases and tumor modeling (two of the major causes of deaths in the advanced countries), a workshop on epidemic modeling, and one on the application of ML and AI in the medical field. Finally, a training school will address the crucial issue of Uncertainty Quantification in Biomedical applications.
Period23 Oct 202308 Dec 2023
Event typeOther
LocationLinz, AustriaShow on map
Degree of RecognitionInternational

Fields of science

  • 101027 Dynamical systems
  • 102003 Image processing
  • 102023 Supercomputing
  • 102001 Artificial intelligence
  • 101004 Biomathematics
  • 102035 Data science
  • 101014 Numerical mathematics
  • 101028 Mathematical modelling
  • 101013 Mathematical logic
  • 102009 Computer simulation
  • 101 Mathematics
  • 202027 Mechatronics
  • 102019 Machine learning
  • 101024 Probability theory
  • 206003 Medical physics
  • 206001 Biomedical engineering
  • 101020 Technical mathematics

JKU Focus areas

  • Digital Transformation