- To give student basic understanding of systems engineering science.
- To introduce the student to the basic principles of system modeling.
- To help student identify the main applications and challenges of systems engineering.
- To provide the student with basic software knowledge of the system modeling language (sysML) for modeling real world systems.
Topics Covered: Introduction to systems engineering, introduction to sysML, systems modeling, sysML diagrams, modeling requirements, physical systems, interfaces and constraints, process modelling
To download a copy of the syllabus click here.
My Erdos number 3 through the following chain:
P. Erdos–> R. Graham–> P. M. Pardalos –> P. Xanthopoulos
If you want to learn more about Erdos number click here.
This is my profile on the math genealogy project:
This is the official citation count from Thompson. This count is more selective than google scholar as it counts only the ISI indexed journals:
Google scholar is now supporting citation counting for researchers. However self citations cannot be excluded.
Xanthopoulos, Petros, Pardalos, Panos M., Trafalis, Theodore B.
Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise.
This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems.
This brief will appeal to theoreticians and data miners working in this field.