ELLIIT Focus periods

The guest program within ELLIIT has the goal to unite high-level international researchers and inspire young ELLIIT researchers through personal contacts with top international researchers.  The most important instrument for the ELLIIT guest program is the 5-week focus period, organized twice a year at one of the ELLIIT universities. We strive to create an environment where ELLIIT researchers can work under optimal circumstances in collaboration with other internationally established scientists around a specific research topic. Researchers within ELLIIT are always welcome to contact us to discuss topics for upcoming focus periods. 

Focus Periods Directors Anders Rantzer, LU, and Fredrik Heintz, LiU

Anders Rantzer

Phone: +46 46 222 87 78

Fredrik Heintz

Phone: +46 13 28 24 28

Spring 2022 – Lund

Data-driven modelling and learning for cancer immunotherapy

Checkpoint inhibitor therapy is a form of cancer treatment that works tremendously well in approximately 30 % of subjects, while being largely ineffective in others. Understanding when and why the therapy works has large implications on the treatment of cancer. In spring 2022, the first ELLIIT focus period will look deeper into this challenge.

For 5 weeks, in the prestigious university town Lund, the focus period will unite young international scholars, ELLIIT researchers and other top international academics active in domains such as:

  • Systems biology
  • Cancer treatment
  • Checkpoint inhibitor therapy
  • Machine learning
  • Dynamical systems
  • Deep learning

During this period, the researchers will work together on jointly defined research topics. In addition to seminars and joint research challenges using unique data sets, a larger workshop with invited top-level scientists will provide knowledge exchange and networking opportunities. The aim of the focus period is to forge new, long lasting, collaborations based on life science research that use and drive the development of novel machine learning techniques.

The full focus period spans April 19 – May 20, 2022.

    Fall 2022 – Linköping

    Hybrid AI – Where data-driven and model-based methods meet

    Data-driven machine learning presents many challenges such as how to deal abstract and causal aspects, how to make learning work with significantly less data like humans can do, and how to achieve robust systems which provides formal guarantees and interpretability. Traditional model- or knowledge-based methods are designed to deal with many of these issues: They deal effectively with generality, abstraction, and causality with strong formal guarantees. A current trend in AI and machine learning today is therefore how to combine these different approaches in a principled and effective way. This is often called Hybrid AI.