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Arturo Ortiz
Mexican Petroleum Institute
Mexico City, Mexico

Throughout his life, Arturo Ortiz-Tapia has found that doing mathematics is not only about developing the subject itself, but also about communicating it to people who are not necessarily mathematics colleagues. There are several periods in his life where he worked as a science popularizer in general, and as mathematical expositor in particular.

Arturo first read about Martin Gardner’s magical mathematics when he was only eight years old. Ever since, he’s been convinced that explaining how you arrive at a brilliant mathematical conclusion is as important as the conclusion itself. The cognitive scaffolding matters. He has been inspired by many science popularizers: Carl Sagan, Hannah Fry, Stephen Hawking, Marcus du Sautoy, and Julieta Fierro, to name just a few. The inspiration he got from them led to his commitment to becoming a better expositor in education (especially now, as a high school teacher). At this point in his life, Arturo is embracing his desire to become a full-time teacher.

Most of Arturo’s working life has dealt with the development of systematic modeling, in particular mathematical modeling of deterministic and/or stochastic systems. As a scientific researcher at the Mexican Petroleum Institute (IMP) for over 16 years, he applied systematic modeling to several problems concerning flow and transport in porous media, that is, multi-physics problems. Thus, even though the research conceptually started with physical phenomena, most of his time was spent applying mathematics in one way or another. He often manipulated data for further analysis, including the computational implementation of the numerical models. Although primarily concerned with research, the results were aimed at given projects, and thus he could do data and modeling analysis targeted to a given purpose.

Arturo teaches, tutors, and mentors mostly, but not exclusively, in mathematics. His first assistant professor position was at the Technological Institute of Celaya teaching linear algebra. He is occasionally invited by local high schools to give a conference on a requested subject. Arturo has directed two engineering dissertations and advised more than 20 student projects. He believes teaching is more than just knowing the subject; it is also knowing the cognitive processes that students undergo, in order to pinpoint any difficulties they might have.

In 2001, Arturo received his PhD in physics from Czech Technical University in Prague. He has been a reviewer for Computing Reviews since 2007, with more than 50 reviews.


Predictive models for decision support in the COVID-19 crisis
Marques J., Gois F., Xavier-Neto J., Fong S., Springer, Switzerland, 2020. 208 pp.  Type: Book (978-3-030619-12-1)

The COVID-19 pandemic is not the first pandemic to have occurred in the history of humanity, but it is the first one where it is possible to use a vast array of mathematical tools to describe, analyze, and even attempt to forecast poss...


Back to the future
Cerf V. Communications of the ACM 62(6): 7-7, 2019.  Type: Article

The name of this short article comes from the fact that the dawn of the Internet with the Advanced Research Projects Agency Network (ARPANET) reintroduced the store-and-forward method used for telegraphs, but instead of human nodes tha...


IBM: the rise and fall and reinvention of a global icon
Cortada J., The MIT Press, Cambridge, MA, 2019. 752 pp.  Type: Book (978-0-262039-44-4)

This work explains that IBM’s success is due to its culture of “THINK,” more specifically to “think,” “learn,” and “take action,” based on data a...


Theory of modeling and simulation: discrete event & iterative system computational foundations (3rd ed.)
Zeigler B., Muzy A., Kofman E., Academic Press, Inc., San Diego, CA, 2019. 692 pp.  Type: Book (978-0-128133-70-5)

By the authors’ reckoning, modeling needs an established body of knowledge, usable by all specialists in the discipline....


Mixture models and applications
Bouguila N., Fan W., Springer International Publishing, New York, NY, 2019. 355 pp.  Type: Book (978-3-030238-75-9)

Mixture models refer to the fact that many datasets have an internal structure that can be better analyzed with more than one probability distribution as models for the data. These two (or more) models may be more or less mixed in term...


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