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.

Contributions

  • We introduced the field of Algorithmic Information Dynamics [see J31, J32, J30, J29 and online course]
  • We have introduced a technical tweak to genetic network reconstruction using different methods of differentiation [see J27].
  • We have introduced novel and more powerful measures to what we call algorithmic machine learning in two areas of specific application (and more are coming), that is, dimension reduction (specially for sparsifying graphs and networks, see J33) and for generative mechanism deconvolution (see J34).
  • We have shown that algorithmic probability may explain phenomenology associated to biological evolution, in particular accelerating convergence rates, promoting memory and producing modularity reminiscent of genetic code, and making massive extinctions a mechanism of evolution and thus potentially providing an intrinsic explanation for them (see J30). 
  • We have shown that indexes based on Shannon Entropy cannot be considered robust measures of randomness especially in the context of maximal entropy models [see J25, P12, J29 and J34].
  • We have shown that computability, causality and dynamical systems are deeply connected by way of algorithmic probability [J, 22, P7, J4].
  • We have introduced a minimal mathematical model for Multiple Sclerosis that displays the most important stages and dynamic features of the disease [J24].
  • We have introduced novel methods can steer and reprogram networks and systems, both biological and synthetic. [see J17, P11, J4, online and online].
  • How natural selection may harness computation and how algorithmic dynamics can explain aspects of biological evolution and , such as speed of convergence, natural mass extinctions and diversity explosions, among others. By taking organisms moving in software space we have always defined a concept of back mutations, this is mutations that are algorithmically more likely to occur because they are in the computationally evolutionary pathway of the system. [online]
  • How our indexes can help in challenges of structural biology such as nucleosome occupancy/positioning. [online]
  • We also introduced numerical improvements and a precise calculation pipeline to infer and reverse engineer networks based on adaptive differentiation [see paper J27].

 We aim at connecting computation, algorithmic information and dynamical systems to reveal the causal content and generating mechanisms and models of synthetic and biological systems.

​For more information, visit Publications.
We have created these videos to help explain our research interests and contributions:
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:
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