Håkon Austlid Tasken

• Lai, Xiaoran; Taskén, Håkon Austlid; Mo, Torgeir; Funke, Simon Wolfgang; Frigessi, Arnoldo & Rognes, Marie Elisabeth [Vis alle 7 forfattere av denne artikkelen] (2021). A scalable solver for a stochastic, hybrid cellular automaton model of personalized breast cancer therapy. International Journal for Numerical Methods in Biomedical Engineering. ISSN 2040-7939. 38(1). doi: 10.1002/cnm.3542. Fulltekst i vitenarkiv Vis sammendrag

  Mathematical modeling and simulation is a promising approach to personalizedcancer medicine. Yet, the complexity, heterogeneity and multi-scale nature ofcancer pose significant computational challenges. Coupling discrete cell-basedmodels with continuous models using hybrid cellular automata (CA) is a power-ful approach for mimicking biological complexity and describing the dynamicalexchange of information across different scales. However, when clinically rele-vant cancer portions are taken into account, such models become computation-ally very expensive. While efficient parallelization techniques for continuousmodels exist, their coupling with discrete models, particularly CA, necessitatesmore elaborate solutions. Building upon FEniCS, a popular and powerful scien-tific computing platform for solving partial differential equations, we developedparallel algorithms to link stochastic CA with differential equations (https://bitbucket.org/HTasken/cansim). The algorithms minimize the communicationbetween processes that share CA neighborhood values while also allowing forreproducibility during stochastic updates. We demonstrated the potential of oursolution on a complex hybrid cellular automaton model of breast cancer treatedwith combination chemotherapy. On a single-core processor, we obtained nearlylinear scaling with an increasing problem size, whereas weak parallel scalingshowed moderate growth in solving time relative to increase in problem size.Finally, we applied the algorithm to a problem that is 500 times larger than previ-ous work, allowing us to run personalized therapy simulations based on hetero-geneous cell density and tumor perfusion conditions estimated from magneticresonance imaging data on an unprecedented scale

Se alle arbeider i NVA

• Dikova, Maria & Tasken, Håkon Austlid (2025). Integreat. Fulltekst i vitenarkiv Vis sammendrag

  Presentation of Integreat: The Norwegian Centre for Knowledge-driven Machine Learning

• Tasken, Håkon Austlid & Arnstad, August (2025). Deepseek V3 and R1. Fulltekst i vitenarkiv Vis sammendrag

  Explain how DeepSeek performs reasoning and techniques it uses for efficient training.

• Tasken, Håkon Austlid (2025). Advance chatbot use - with RAG example. Fulltekst i vitenarkiv Vis sammendrag

  Leveraging chatbots to perform advanced tasks for the layman, and knowing when their use is appropriate.

• Tasken, Håkon Austlid (2025). Resonneringsmodeller - KI som tenker seg om. Fulltekst i vitenarkiv Vis sammendrag

  Innblikk i hvordan språkmodeller har fått evnen til å tenke og resonnere gjennom å ha en slags indre monolog det siste året, slik at de kan løse vanskeligere problemer.

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