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Hector Zenil
Oxford Immune Algorithmics
Oxford, United Kingdom

Hector Zenil received his bachelor’s degree in math from the National University of Mexico (UNAM), his master's degree in logic from the Sorbonne in Paris, and his PhD in computer science from the University of Lille 1, France (2011). After graduation, he did postdoctoral research at the Behavioural and Evolutionary Theory Lab of the University of Sheffield in the UK. He currently conducts research in information theory and computational biology at the Unit of Computational Medicine of the Karolinska Institute in Stockholm, working on cutting-edge applications of complexity science to genomics and network biology.

He is also the head of the Algorithmic Nature Group (the lab responsible for the Online Algorithmic Complexity Calculator and the Human Randomness Generation Project), co-director of the French-based lab LABORES For the Natural and Digital Sciences, and senior research associate and consultant for Wolfram Research (the creators of Mathematica).

He has held various visiting positions in the US. He was a visiting scholar in 2008 at Carnegie Mellon University and in 2007 at MIT, associated with the NASA Mars Gravity Biosatellite project. In 2012, he was invited to become a member of the Turing Centenary Advisory Committee in the UK and was accepted to Mexico's SNI (a distinguished list of appointed national researchers).

He is the editor of the books Randomness Through Computation (World Scientific), A Computable Universe (World Scientific, with a foreword by Sir Roger Penrose), and Irreducibility and Computational Equivalence (Springer, with a foreword by Greg Chaitin and an afterword by Cris Calude).

He has been a reviewer for Computing Reviews since 2003, and has written over 40 book and paper reviews.


Deep learning for medical decisions
Kose U., Deperlioglu O., Alzubi J., Patrut B.,  Springer International Publishing, New York, NY, 2020. 189 pp. Type: Book (978-9-811563-24-9)

I was very excited about reviewing this book and writing what I thought would be a great summary on the advances of deep learning in medical decision support. However, the text is so poorly written that it is almost impossible to read or make sens...


Machine learning paradigms: artificial immune systems and their applications in software personalization
Sotiropoulos D., Tsihrintzis G.,  Springer International Publishing, New York, NY, 2016. 327 pp. Type: Book (978-3-319471-92-1)

Machine learning paradigms is an excellent introduction to both immunology from a computer scientist point of view and to algorithms designed and inspired by the biological immune system....


Turing’s vision: the birth of computer science
Bernhardt C.,  The MIT Press, Cambridge, MA, 2016. 208 pp. Type: Book (978-0-262034-54-8)

Books about Alan Turing’s life and work have always gone to one of two extremes, either to the complete technical or to the complete trivial focusing on, for example, Turing’s sexual preferences, which should be completely irrelevant a...


Causal case study methods: foundations and guidelines for comparing, matching, and tracing
Beach D., Pedersen R.,  University of Michigan Press, Ann Arbor, MI, 2016. 398 pp. Type: Book (978-0-472053-22-3)

The book is a treatise on some important aspects of causality from epistemological and ontological points of view, particularly chapters 1 through 5 on understanding, defining, and measuring causality. Except for chapters 4 and 5, chapters 1 to 6 ...


Natural computing algorithms
Brabazon A., O’Neill M., McGarraghy S.,  Springer Publishing Company, Incorporated, New York, NY, 2015. 554 pp. Type: Book (978-3-662436-30-1)

A combination of old, sometimes revisited, and new natural computing algorithms, this book is arranged almost chronologically. Part 1, for example, is about the now old discipline of evolutionary computation and genetic algorithms, where one can f...


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