Methodological fields

A systems design engineering perspective is used to build models and simulate static and dynamic scenarios. Topics include design, performance modelling, optimization, system taxonomies, uncertainty modelling, reliability analysis to name a few.

The development of image and signal processing techniques to interpret the world around us through digital images, sensor signals  and videos using signal processing techniques to help analyze, classify, interpret and control.

Machine Learning (statistical learning, deep networks, neural nets, etc) meta-heuristics (evolutionary algorithms, etc) and knowledge-based methods (fuzzy & probabilistic) are used to solve problems that cannot be adequately solved via traditional methods.

Decision making models simulate cognitive processes used in the selection of a belief or a course of actions among several alternative possibilities. Optimization is a mathematical procedure for finding a maximum or minimum value of a function of several variables subject to a set of constraints.

Application fields

Biomedical engineering is the application of systems engineering principles and design concepts to medicine and biology for healthcare diagnosis therapy or prevention. Emphasis is placed on biomechanics, biosignals, biodevices as well as biomedical design.

A systems design engineering approach is applied to the understanding of interactions between humans and other system elements.  Observations, measurements, theory and methods are brought together to analyze and design systems that optimize performance that considers human well being.

Mechatronics is a multi-disciplinary field that intersects mechanical, electrical and software engineering. Physical systems focuses on models that aid in understanding realizable engineering systems.

Methodologies and techniques are researched for formally modelling societal & environmental systems from a systems design engineering perspective.