CESF is leading in the development of new required doctoral subjects, teaching 'fundamentals' associated with Engineering Systems.

The first such subject is:
ESD.86 Models, Data, Inference for Socio-Technical Systems (New) Prereq: ESD.83, 6.041G (Spring) 3-0-9

Its description is as follows: Use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Enhance model-building skills, including: review and extension of functions of random variables, Poisson processes, and Markov processes. Move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables. Review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. Class project. Richard C. Larson, Daniel D. Frey

This subject operates in parallel along several tracks. There are the usual lectures, with weekly recitations and problem sets. There is also an ongoing project to monitor uses and abuses and misinterpretations of statistics in the widely read U.S. media. Then there is a class project. The subject is not class in statistics nor a class in applied probability. Rather, it is an attempt to merge the two, to show how one can build models axiomatically from basic principals of applied probability and/or one can build models by 'curve fitting' using data sets and the theory of statistics. But we emphasize that it is best to use a mixture of both approaches, especially to avoid common pitfalls such as confusing correlation with causality.

Our hope is that additional doctoral level subjects in fundamentals will be developed by others in the years ahead.