Algorithmic Information Dynamics (or Algorithmic Dynamics in short) is thus a new type of discrete calculus based on computer programming to study causation by generating mechanistic models to help find first principles for physical phenomena building up the next generation of data analytics in what we call Algorithmic Machine Learning. Teaming up with immunologists, bioinformaticians, toxicologists, oncologists, cognitive scientists and molecular biologists, our lab applies all these mathematical ideas and fundamental findings to, among other areas: behavioural, evolutionary and molecular biology.
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Contributions |
We aim at connecting computability theory, algorithmic information and dynamical systems to infer causation and generating mechanisms of synthetic and biological systems helping to produce models and reprogramming living systems.
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For more information, visit Publications. |
We have produced these videos to help explain our research interests and contributions:
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These videos do not, of course, contain all the details and information that only reading the full papers can provide.
And a teaser of an online course (MOOC) in preparation to come
out in the Sta Fe Institute's Complexity Explorer platform:
out in the Sta Fe Institute's Complexity Explorer platform: